Relevant bibliographies by topics / Label Shift

Academic literature on the topic 'Label Shift'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Label Shift"

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Chow, C. W., and H. K. Tsang. "Orthogonal label switching using polarization-shift-keying payload and amplitude-shift-keying label." IEEE Photonics Technology Letters 17, no. 11 (November 2005): 2475–77. http://dx.doi.org/10.1109/lpt.2005.857590.

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Wetterau, Lukas, Claas Abert, Dieter Suess, Manfred Albrecht, and Bernd Witzigmann. "Micromagnetic Simulations of Submicron Vortex Structures for the Detection of Superparamagnetic Labels." Sensors 20, no. 20 (October 15, 2020): 5819. http://dx.doi.org/10.3390/s20205819.

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We present a numerical investigation on the detection of superparamagnetic labels using a giant magnetoresistance (GMR) vortex structure. For this purpose, the Landau–Lifshitz–Gilbert equation was solved numerically applying an external z-field for the activation of the superparamagnetic label. Initially, the free layer’s magnetization change due to the stray field of the label is simulated. The electric response of the GMR sensor is calculated by applying a self-consistent spin-diffusion model to the precomputed magnetization configurations. It is shown that the soft-magnetic free layer reacts on the stray field of the label by shifting the magnetic vortex orthogonally to the shift direction of the label. As a consequence, the electric potential of the GMR sensor changes significantly for label shifts parallel or antiparallel to the pinning of the fixed layer. Depending on the label size and its distance to the sensor, the GMR sensor responds, changing the electric potential from 26.6 mV to 28.3 mV.
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Yusheng, Cheng, Zhao Dawei, Zhan Wenfa, and Wang Yibin. "Multi-label learning of non-equilibrium labels completion with mean shift." Neurocomputing 321 (December 2018): 92–102. http://dx.doi.org/10.1016/j.neucom.2018.09.033.

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Rezaei, Ashkan, Anqi Liu, Omid Memarrast, and Brian D. Ziebart. "Robust Fairness Under Covariate Shift." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9419–27. http://dx.doi.org/10.1609/aaai.v35i11.17135.

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Making predictions that are fair with regard to protected attributes (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing techniques derive a fair model from sampled labeled data relying on the assumption that training and testing data are identically and independently drawn (iid) from the same distribution. In practice, distribution shift can and does occur between training and testing datasets as the characteristics of individuals interacting with the machine learning system change. We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same. We seek fair decisions under these assumptions on target data with unknown labels. We propose an approach that obtains the predictor that is robust to the worst-case testing performance while satisfying target fairness requirements and matching statistical properties of the source data. We demonstrate the benefits of our approach on benchmark prediction tasks.
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Zhang, Huayi, Lei Cao, Samuel Madden, and Elke Rundensteiner. "LANCET." Proceedings of the VLDB Endowment 14, no. 11 (July 2021): 2154–66. http://dx.doi.org/10.14778/3476249.3476269.

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Cutting-edge machine learning techniques often require millions of labeled data objects to train a robust model. Because relying on humans to supply such a huge number of labels is rarely practical, automated methods for label generation are needed. Unfortunately, critical challenges in auto-labeling remain unsolved, including the following research questions: (1) which objects to ask humans to label, (2) how to automatically propagate labels to other objects, and (3) when to stop labeling. These three questions are not only each challenging in their own right, but they also correspond to tightly interdependent problems. Yet existing techniques provide at best isolated solutions to a subset of these challenges. In this work, we propose the first approach, called LANCET, that successfully addresses all three challenges in an integrated framework. LANCET is based on a theoretical foundation characterizing the properties that the labeled dataset must satisfy to train an effective prediction model, namely the Covariate-shift and the Continuity conditions. First, guided by the Covariate-shift condition, LANCET maps raw input data into a semantic feature space, where an unlabeled object is expected to share the same label with its near-by labeled neighbor. Next, guided by the Continuity condition, LANCET selects objects for labeling, aiming to ensure that unlabeled objects always have some sufficiently close labeled neighbors. These two strategies jointly maximize the accuracy of the automatically produced labels and the prediction accuracy of the machine learning models trained on these labels. Lastly, LANCET uses a distribution matching network to verify whether both the Covariate-shift and Continuity conditions hold, in which case it would be safe to terminate the labeling process. Our experiments on diverse public data sets demonstrate that LANCET consistently outperforms the state-of-the-art methods from Snuba to GOGGLES and other baselines by a large margin - up to 30 percentage points increase in accuracy.
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Chen, Hongwei, Minghua Chen, Ciyuan Qiu, and Shizhong Xie. "Orthogonal polarization shift keying label rewriting method in an all-optical label switching network." Optics Letters 32, no. 9 (April 3, 2007): 1050. http://dx.doi.org/10.1364/ol.32.001050.

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Pikramenos, George, Evaggelos Spyrou, and Stavros J. Perantonis. "Extending Partial Domain Adaptation Algorithms to the Open-Set Setting." Applied Sciences 12, no. 19 (October 6, 2022): 10052. http://dx.doi.org/10.3390/app121910052.

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Partial domain adaptation (PDA) is a framework for mitigating the covariate shift problem when target labels are contained in source labels. For this task, adversarial neural network (ANN) methods proposed in the literature have been proven to be flexible and effective. In this work, we adapt such methods to tackle the more general problem of open-set domain adaptation (OSDA), which further allows the existence of target instances with labels outside the source labels. The aim in OSDA is to mitigate the covariate shift problem and to identify target instances with labels outside the source label space. We show that the effectiveness of ANN methods utilized in the PDA setting is hindered by outlier target instances, and we propose an adaptation for effective OSDA.
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Chen, Jin, Xinxiao Wu, Yao Hu, and Jiebo Luo. "Spatial-temporal Causal Inference for Partial Image-to-video Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1027–35. http://dx.doi.org/10.1609/aaai.v35i2.16187.

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Image-to-video adaptation leverages off-the-shelf learned models in labeled images to help classification in unlabeled videos, thus alleviating the high computation overhead of training a video classifier from scratch. This task is very challenging since there exist two types of domain shifts between images and videos: 1) spatial domain shift caused by static appearance variance between images and video frames, and 2) temporal domain shift caused by the absence of dynamic motion in images. Moreover, for different video classes, these two domain shifts have different effects on the domain gap and should not be treated equally during adaptation. In this paper, we propose a spatial-temporal causal inference framework for image-to-video adaptation. We first construct a spatial-temporal causal graph to infer the effects of the spatial and temporal domain shifts by performing counterfactual causality. We then learn causality-guided bidirectional heterogeneous mappings between images and videos to adaptively reduce the two domain shifts. Moreover, to relax the assumption that the label spaces of the image and video domains are the same by the existing methods, we incorporate class-wise alignment into the learning of image-video mappings to perform partial image-to-video adaptation where the image label space subsumes the video label space. Extensive experiments on several video datasets have validated the effectiveness of our proposed method.
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Smith, Lisa, Kimberly Arcand, Jeffrey Smith, Randall Smith, Jay Bookbinder, and Megan Watzke. "Examining perceptions of astronomy images across mobile platforms." Journal of Science Communication 13, no. 02 (March 25, 2014): A01. http://dx.doi.org/10.22323/2.13020201.

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Modern society has led many people to become consumers of data unlike previous generations. How this shift in the way information is communicated and received — including in areas of science — and affects perception and comprehension is still an open question. This study examined one aspect of this digital age: perceptions of astronomical images and their labels, on mobile platforms. Participants were n = 2183 respondents to an online survey, and two focus groups (n = 12 astrophysicists; n = 11 lay public). Online participants were randomly assigned to 1 of 12 images, and compared two label formats. Focus groups compared mobile devices and label formats. Results indicated that the size and quality of the images on the mobile devices affected label comprehension and engagement. The question label format was significantly preferred to the fun fact. Results are discussed in terms of effective science communication using technology.
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Murza, Kimberly A., and Barbara J. Ehren. "Considering the Language Disorder Label Debate From a School Speech-Language Pathology Lens." Perspectives of the ASHA Special Interest Groups 5, no. 1 (February 21, 2020): 47–54. http://dx.doi.org/10.1044/2019_persp-19-00077.

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Purpose The purpose of this article is to situate the recent language disorder label debate within a school's perspective. As described in two recent The ASHA Leader articles, there is international momentum to change specific language impairment to developmental language disorder . Proponents of this change cite increased public awareness and research funding as part of the rationale. However, it is unclear whether this label debate is worthwhile or even practical for the school-based speech-language pathologist (SLP). A discussion of the benefits and challenges to a shift in language disorder labels is provided. Conclusions Although there are important arguments for consistency in labeling childhood language disorder, the reality of a label change in U.S. schools is hard to imagine. School-based services are driven by eligibility through the Individuals with Disabilities Education Act, which has its own set of labels. There are myriad reasons why advocating for the developmental language disorder label may not be the best use of SLPs' time, perhaps the most important of which is that school SLPs have other urgent priorities.
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Dissertations / Theses on the topic "Label Shift"

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Fonseca, Eduardo. "Training sound event classifiers using different types of supervision." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/673067.

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The automatic recognition of sound events has gained attention in the past few years, motivated by emerging applications in fields such as healthcare, smart homes, or urban planning. When the work for this thesis started, research on sound event classification was mainly focused on supervised learning using small datasets, often carefully annotated with vocabularies limited to specific domains (e.g., urban or domestic). However, such small datasets do not support training classifiers able to recognize hundreds of sound events occurring in our everyday environment, such as kettle whistles, bird tweets, cars passing by, or different types of alarms. At the same time, large amounts of environmental sound data are hosted in websites such as Freesound or YouTube, which can be convenient for training large-vocabulary classifiers, particularly using data-hungry deep learning approaches. To advance the state-of-the-art in sound event classification, this thesis investigates several strands of dataset creation as well as supervised and unsupervised learning to train large-vocabulary sound event classifiers, using different types of supervision in novel and alternative ways. Specifically, we focus on supervised learning using clean and noisy labels, as well as self-supervised representation learning from unlabeled data. The first part of this thesis focuses on the creation of FSD50K, a large-vocabulary dataset with over 100h of audio manually labeled using 200 classes of sound events. We provide a detailed description of the creation process and a comprehensive characterization of the dataset. In addition, we explore architectural modifications to increase shift invariance in CNNs, improving robustness to time/frequency shifts in input spectrograms. In the second part, we focus on training sound event classifiers using noisy labels. First, we propose a dataset that supports the investigation of real label noise. Then, we explore network-agnostic approaches to mitigate the effect of label noise during training, including regularization techniques, noise-robust loss functions, and strategies to reject noisy labeled examples. Further, we develop a teacher-student framework to address the problem of missing labels in sound event datasets. In the third part, we propose algorithms to learn audio representations from unlabeled data. In particular, we develop self-supervised contrastive learning frameworks, where representations are learned by comparing pairs of examples computed via data augmentation and automatic sound separation methods. Finally, we report on the organization of two DCASE Challenge Tasks on automatic audio tagging with noisy labels. By providing data resources as well as state-of-the-art approaches and audio representations, this thesis contributes to the advancement of open sound event research, and to the transition from traditional supervised learning using clean labels to other learning strategies less dependent on costly annotation efforts.
El interés en el reconocimiento automático de eventos sonoros se ha incrementado en los últimos años, motivado por nuevas aplicaciones en campos como la asistencia médica, smart homes, o urbanismo. Al comienzo de esta tesis, la investigación en clasificación de eventos sonoros se centraba principalmente en aprendizaje supervisado usando datasets pequeños, a menudo anotados cuidadosamente con vocabularios limitados a dominios específicos (como el urbano o el doméstico). Sin embargo, tales datasets no permiten entrenar clasificadores capaces de reconocer los cientos de eventos sonoros que ocurren en nuestro entorno, como silbidos de kettle, sonidos de pájaros, coches pasando, o diferentes alarmas. Al mismo tiempo, websites como Freesound o YouTube albergan grandes cantidades de datos de sonido ambiental, que pueden ser útiles para entrenar clasificadores con un vocabulario más extenso, particularmente utilizando métodos de deep learning que requieren gran cantidad de datos. Para avanzar el estado del arte en la clasificación de eventos sonoros, esta tesis investiga varios aspectos de la creación de datasets, así como de aprendizaje supervisado y no supervisado para entrenar clasificadores de eventos sonoros con un vocabulario extenso, utilizando diferentes tipos de supervisión de manera novedosa y alternativa. En concreto, nos centramos en aprendizaje supervisado usando etiquetas sin ruido y con ruido, así como en aprendizaje de representaciones auto-supervisado a partir de datos no etiquetados. La primera parte de esta tesis se centra en la creación de FSD50K, un dataset con más de 100h de audio etiquetado manualmente usando 200 clases de eventos sonoros. Presentamos una descripción detallada del proceso de creación y una caracterización exhaustiva del dataset. Además, exploramos modificaciones arquitectónicas para aumentar la invariancia frente a desplazamientos en CNNs, mejorando la robustez frente a desplazamientos de tiempo/frecuencia en los espectrogramas de entrada. En la segunda parte, nos centramos en entrenar clasificadores de eventos sonoros usando etiquetas con ruido. Primero, proponemos un dataset que permite la investigación del ruido de etiquetas real. Después, exploramos métodos agnósticos a la arquitectura de red para mitigar el efecto del ruido en las etiquetas durante el entrenamiento, incluyendo técnicas de regularización, funciones de coste robustas al ruido, y estrategias para rechazar ejemplos etiquetados con ruido. Además, desarrollamos un método teacher-student para abordar el problema de las etiquetas ausentes en datasets de eventos sonoros. En la tercera parte, proponemos algoritmos para aprender representaciones de audio a partir de datos sin etiquetar. En particular, desarrollamos métodos de aprendizaje contrastivos auto-supervisados, donde las representaciones se aprenden comparando pares de ejemplos calculados a través de métodos de aumento de datos y separación automática de sonido. Finalmente, reportamos sobre la organización de dos DCASE Challenge Tasks para el tageado automático de audio a partir de etiquetas ruidosas. Mediante la propuesta de datasets, así como de métodos de vanguardia y representaciones de audio, esta tesis contribuye al avance de la investigación abierta sobre eventos sonoros y a la transición del aprendizaje supervisado tradicional utilizando etiquetas sin ruido a otras estrategias de aprendizaje menos dependientes de costosos esfuerzos de anotación.
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Caye, Daudt Rodrigo. "Convolutional neural networks for change analysis in earth observation images with noisy labels and domain shifts." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT033.

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L'analyse de l'imagerie satellitaire et aérienne d'observation de la Terre nous permet d'obtenir des informations précises sur de vastes zones. Une analyse multitemporelle de telles images est nécessaire pour comprendre l'évolution de ces zones. Dans cette thèse, les réseaux de neurones convolutifs sont utilisés pour détecter et comprendre les changements en utilisant des images de télédétection provenant de diverses sources de manière supervisée et faiblement supervisée. Des architectures siamoises sont utilisées pour comparer des paires d'images recalées et identifier les pixels correspondant à des changements. La méthode proposée est ensuite étendue à une architecture de réseau multitâche qui est utilisée pour détecter les changements et effectuer une cartographie automatique simultanément, ce qui permet une compréhension sémantique des changements détectés. Ensuite, un filtrage de classification et un nouvel algorithme de diffusion anisotrope guidée sont utilisés pour réduire l'effet du bruit d'annotation, un défaut récurrent pour les ensembles de données à grande échelle générés automatiquement. Un apprentissage faiblement supervisé est également réalisé pour effectuer une détection de changement au niveau des pixels en utilisant uniquement une supervision au niveau de l'image grâce à l'utilisation de cartes d'activation de classe et d'une nouvelle couche d'attention spatiale. Enfin, une méthode d'adaptation de domaine fondée sur un entraînement adverse est proposée. Cette méthode permet de projeter des images de différents domaines dans un espace latent commun où une tâche donnée peut être effectuée. Cette méthode est testée non seulement pour l'adaptation de domaine pour la détection de changement, mais aussi pour la classification d'images et la segmentation sémantique, ce qui prouve sa polyvalence
The analysis of satellite and aerial Earth observation images allows us to obtain precise information over large areas. A multitemporal analysis of such images is necessary to understand the evolution of such areas. In this thesis, convolutional neural networks are used to detect and understand changes using remote sensing images from various sources in supervised and weakly supervised settings. Siamese architectures are used to compare coregistered image pairs and to identify changed pixels. The proposed method is then extended into a multitask network architecture that is used to detect changes and perform land cover mapping simultaneously, which permits a semantic understanding of the detected changes. Then, classification filtering and a novel guided anisotropic diffusion algorithm are used to reduce the effect of biased label noise, which is a concern for automatically generated large-scale datasets. Weakly supervised learning is also achieved to perform pixel-level change detection using only image-level supervision through the usage of class activation maps and a novel spatial attention layer. Finally, a domain adaptation method based on adversarial training is proposed, which succeeds in projecting images from different domains into a common latent space where a given task can be performed. This method is tested not only for domain adaptation for change detection, but also for image classification and semantic segmentation, which proves its versatility
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Books on the topic "Label Shift"

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Cinquegrani, Alessandro, Francesca Pangallo, and Federico Rigamonti. Romance e Shoah Pratiche di narrazione sulla tragedia indicibile. Venice: Fondazione Università Ca’ Foscari, 2021. http://dx.doi.org/10.30687/978-88-6969-492-9.

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Over the last 70 years, Holocaust representations increased significantly as cultural objects distributed on a large scale: fictional books, museum sites, artworks, documentaries, and films are only a few samples of those echoes the Holocaust produced in contemporary Western culture. There are some specific patterns in the way the Holocaust has been represented that, however, contrast with the survivors’ account of the same event: for example, the dichotomy between bad and good characters so essential within Holocaust-based media – especially on television and film - does not really match with the testimony’s experience. While storytelling strategies may help to involve the public by emotionally engaging with the story, the risks of altering the real meaning of the Holocaust are quite high: what we often label as a “story” is actually been an outrageous, documented mass-genocide. Furthermore, as the age gap between the present and the past generation progresses, also the collective awareness of Nazi crimes as a real fact gets compromised. This volume explores selected Holocaust narrations by contextualizing the historical, literary, and social influences those texts had in their unique points of view. Starting with some recent examples of Holocaust exploitation through social media, the first chapter explores the paradigm shift when the Holocaust became a cultural, fictional trend rather than a historical massacre. In the second chapter, the analysis examines postmodern representations of Holocaust and Nazi semantics through relevant examples taken from both American and European literature. The third chapter analyses Europe Central by William T. Vollman, as all the narratological and cultural issues considered in the previous two chapters are well outlined in this articulated novel, where the relationship between reality and its representation after the postmodernist period is largely investigated. In chapter four, an account is given of the connections and differences between the narratological category romance, as understood by Northrop Frye, and Holocaust narration features. In chapter five, those elements are used to consider the work of Italian Holocaust survivor and Jewish writer Primo Levi, as his narration around Auschwitz adopts some fictional tools and still refuses undemanding storytelling mechanisms. The sixth and final chapter examines the relevant novel Les Benviellants by Jonathan Littell, considering its Nazi genocide account through the antagonist’s perspective.
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Sands, Bonny. The Challenge of Documenting Africa’s Least-Known Languages. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190256340.003.0002.

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The lack of adequate documentation for African languages is a major challenge facing linguists. Understandably, priority has often been given to the most endangered languages, but the level of language endangerment in Africa has been grossly underestimated. Language shift can occur in a single generation, so population surveys that are 20 or more years old cannot be used to label a language “safe,” particularly when it is used by only a few thousand people. High rates of language shift are being reported in different parts of the continent, and even larger languages (with 100,000 or more speakers) might best be considered threatened. Documenting an obsolescent language is a difficult task, made even more difficult if the language is essentially undescribed. Since basic phonological and grammatical sketches are lacking for so many African languages, we should try to address the challenge of documenting these poorly known languages while they are still used by all generations.
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Day, Gloria. Weight of Labels: A Poetic Display of an Internal Shift. Lulu Press, Inc., 2018.

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Kahlos, Maijastina. Religious Dissent in Late Antiquity, 350-450. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190067250.001.0001.

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Religious Dissent in Late Antiquity reconsiders the religious history of the late Roman Empire, focusing on the shifting position of dissenting religious groups. The groups under consideration are non-Christians (‘pagans’) and deviant Christians (‘heretics’). The period from the mid-fourth century until the mid-fifth century CE witnessed a significant transformation of late Roman society and a gradual shift from the world of polytheistic religions into the Christian Empire. This book demonstrates that the narrative is much more nuanced than the simple Christian triumph over the classical world. It looks at everyday life, economic aspects, day-to-day practices, and conflicts of interest in the relations of religious groups. The book addresses two aspects: rhetoric and realities, and consequently delves into the interplay between the manifest ideologies and daily life found in late antique sources. We perceive constant flux between moderation and coercion that marked the relations of religious groups, both majorities and minorities, as well as the imperial government and religious communities. Religious Dissent in Late Antiquity is a detailed analysis of selected themes and a close reading of selected texts, tracing key elements and developments in the treatment of dissident religious groups. The book focuses on specific themes, such as the limits of imperial legislation and ecclesiastical control, the end of sacrifices, and the label of magic. It also examines the ways in which dissident religious groups were construed as religious outsiders in late Roman society.
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Figone, Albert J. Do No Evil, See No Evil, and Hear No Evil. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037283.003.0004.

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This chapter shifts the focus from the players to the coaches. After the basketball scandal broke in January of 1951, colleges, with the aid of many writers, were quick to label the players' misdeeds “criminal” and to attribute them to players' lack of moral values and flawed characters. Yet the blame for the pervasive corruption in college athletics did not rest on the shoulders of the athletes alone. The chapter argues that the college coaches, administrations, and other such authorities were also in part responsible for the gambling issue, although unlike the players, they were largely able to escape the taint of scandal. Thus, this chapter argues that how basketball coaches made their choice to ignore game fixing reveals the essential role their passive complicity played in the size and shape of the scandals.
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Haig, Geoffrey. Deconstructing Iranian Ergativity. Edited by Jessica Coon, Diane Massam, and Lisa Demena Travis. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198739371.013.20.

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This chapter provides an overview of the alignment splits found in most Iranian languages, focussing on their historical emergence, and their currently attested variability. Following Haig (2008), the origins of ergativity in Iranian are linked to pre-existing, non-canonical subject constructions typically involving Benefactives, External Possessors, and Experiencers, which then extended to clauses with participial predicates expressing agentive semantics. The current variation found in the ergative-like constructions is illustrated through three case-studies of dialectal microvariation: Kurdish, Balochi, and Taleshi. It is argued that the variation in the ergative constructions of the modern languages should be viewed as resulting from the interplay of partially independent changes working through distinct sub-systems, in particular case-marking, agreement, and pronominal clitic systems, rather than in terms of monolithic shifts from one alignment type to another. From this perspective, ergativity is merely a taxonomic label for a particular constellation of case and agreement features, with no more theoretical significance than any of the other attested constellations.
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Lundén, Elizabeth Castaldo. Fashion on the Red Carpet. Edinburgh University Press, 2021. http://dx.doi.org/10.3366/edinburgh/9781474461801.001.0001.

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The Academy Awards’ red-carpet is the most prominent fashion show in media culture. This book investigates the historical liaison between Hollywood and fashion institutions to describe how public relations campaigns and the media articulated fashion discourses around the Oscars throughout history. It argues that the fashion industry’s business model of celebrity endorsement and renowned designers as branded labels is based on the triangulation done by Hollywood studios, department stores, and American garment manufacturers during the interwar era. Departing from archival sources, and tracing discourses of fashion, stardom, and celebrity around Hollywood and the Oscars, this study unravels this phenomenon’s cultural, political and economic impact, explaining how the Academy Awards’ red-carpet became a marquee for the global endorsement of high-end fashion brands. The book addresses globalisation as a central topic to frame the red-carpet phenomenon, linking the fashion and media industries throughout the 20th Century. It points at the postwar as a historical turning point that consolidated the position of the United States as a veritable behemoth exporter of popular culture, depicting the American lifestyle as synonymous with wealth and comfort to further the global expansion of consumer culture. The book identifies power shift towards television, the emergence of celebrity culture, the post-war reactivation of transatlantic trade, the growth of fashion journalism, and the increasing circulation of designer names in the media as a series of converging factors that led to the institutionalisation of the red-carpet parade as a fashion event in its own right.
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Book chapters on the topic "Label Shift"

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Hwang, Sehyun, Sohyun Lee, Sungyeon Kim, Jungseul Ok, and Suha Kwak. "Combating Label Distribution Shift for Active Domain Adaptation." In Lecture Notes in Computer Science, 549–66. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19827-4_32.

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Rodriguez-Furlan, Cecilia, and Glenn R. Hicks. "Label-Free and Confirmation Using Thermal Stability Shift Assays." In Methods in Molecular Biology, 163–73. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0954-5_14.

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Ma, Wenao, Cheng Chen, Shuang Zheng, Jing Qin, Huimao Zhang, and Qi Dou. "Test-Time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift." In Lecture Notes in Computer Science, 313–23. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16437-8_30.

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Doležel, Michal. "Defining TestOps: Collaborative Behaviors and Technology-Driven Workflows Seen as Enablers of Effective Software Testing in DevOps." In Agile Processes in Software Engineering and Extreme Programming – Workshops, 253–61. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58858-8_26.

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Abstract Context: DevOps is an increasingly popular approach to software development and software operations. Being understood as mutually integrated, both activities have been re-united under one single label. In contrast to traditional software development activities, DevOps promotes numerous fundamental changes, and the area of software testing is not an exception. Yet, the exact appearance of software testing within DevOps is poorly understood, so is the notion of TestOps. Objective: This paper explores TestOps as a concept rooted in industrial practice. Method: To provide a pluralist outline of practitioners’ views on What is TestOps, the YouTube platform was searched for digital content containing either “TestOps” or “DevTestOps” in the content title. Through a qualitative lens, the resulting set was systematically annotated and thematically analyzed in an inductive manner. Results: Referring to DevOps, practitioners use the notion of TestOps when characterizing a conceptual shift that occurs within the area of software testing. As a matter of fact, two dominant categories were found in the data: (i) TestOps as a new organizational philosophy; (ii) TestOps as an innovative software technique (i.e. process supported by technology). A set of high-level themes within each of these categories was identified and described. Conclusion: The study outlines an inconsistency in practitioner perspectives on the nature of TestOps. To decrease the identified conceptual ambiguity, the proposed model posits two complementary meanings of TestOps.
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Kim, Dongwan, Yi-Hsuan Tsai, Yumin Suh, Masoud Faraki, Sparsh Garg, Manmohan Chandraker, and Bohyung Han. "Learning Semantic Segmentation from Multiple Datasets with Label Shifts." In Lecture Notes in Computer Science, 20–36. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19815-1_2.

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Filbrandt, Gregory, Konstantinos Kamnitsas, David Bernstein, Alexandra Taylor, and Ben Glocker. "Learning from Partially Overlapping Labels: Image Segmentation Under Annotation Shift." In Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health, 123–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87722-4_12.

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Manakov, Ilja, Markus Rohm, Christoph Kern, Benedikt Schworm, Karsten Kortuem, and Volker Tresp. "Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation." In Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data, 3–10. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33391-1_1.

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Corens, Liesbeth. "Introduction." In Confessional Mobility and English Catholics in Counter-Reformation Europe, 1–20. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198812432.003.0008.

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The introduction sets out the methodological framework, and expounds the conceptual shift from exile to confessional mobility. Without summarily dismissing ‘exile’, it argues that adopting a wider term changes perspective, reshapes what we see in the sources, and opens up new questions. Rather than labelling people and reducing their entire identity to that label as their key characteristic, ‘confessional mobility’ focuses on the activity in which complex and conflicted people engage. In the process, it does justice to the broad spectrum of English Catholics who crossed the Channel, challenging scholarship that searches for a single ‘pure’ motivation for travel. Moreover, scrutinizing mobility forgoes an assumed separation and isolation, highlighting the constant cross-Channel communication. Links with the Counter-Reformation on the Continent were not important solely for those who travelled abroad, nor solely for their time abroad: they influenced the entire Catholic community.
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Lépinard, Éléonore. "Feminist Whiteness." In Feminist Trouble, 81–126. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190077150.003.0004.

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This chapter focuses on feminist whiteness, a concept it introduces and defines as the product of a process of political subjectivation as a white feminist. The concept captures the various repertoires that white feminists elaborate to talk about—or rather actively ignore—race relations of power and their own privileged positions in this racial order. The chapter traces how white feminists are constituted as political subjects through their relationship to nonwhite feminists, and to those whom they perceive and label as “bad” feminist subjects. It shows that debates on Islamic veiling have operated a shift in feminist whiteness, from feminist whiteness as ignorance to feminist whiteness as an active participation in national identity and femonationalist discourses. It also shows that feminist whiteness is multiple and varies across contexts. In France and Quebec, white feminists use different repertoires to address race issues. Some work around or evade race, while others recognize its political salience. These different forms of feminist whiteness are articulated with specific moral dispositions and emotions.
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Treacy, Corbin. "Writing in the Aftermath of Two Wars: Algerian Modernism and the Génération ’88." In Algeria. Liverpool University Press, 2017. http://dx.doi.org/10.5949/liverpool/9781786940216.003.0007.

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Algerian literary works from the civil war of the 1990s are often described as testimonial—a littérature d’urgence. While the label ignores many experimental and anti-representational works from this period, the décennie noire clearly weighed on authors and provoked particular aesthetic responses. Less has been said of Algerian cultural production from the years following the civil war. Algerian writers have started to leverage fantasy, myth, and the fable to respond to the increasingly surreal relationship between state and society. This article addresses the shift from realism to surrealism in contemporary Algerian fiction, with special attention to the ways in which less representational texts more fully adumbrate the particularities of the Bouteflika era. Specifically, I focus on works by Mustapha Benfodil and Kamel Daoud, two authors born after independence who continue to live, write, and publish in Algeria. Their affiliation with Éditions Barzakh—an independent Algerian publisher — has granted their work the freedom to deviate from the proscribed narratives of terrorism and victimhood more common to Algeria’s export literature. I argue that Daoud and Benfodil create alternative forms of literary engagement that articulate a revised Algerian nationalism, plotting paths to futures beyond the limiting terms of the static present.
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Conference papers on the topic "Label Shift"

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Liu, Xiaofeng, Bo Hu, Linghao Jin, Xu Han, Fangxu Xing, Jinsong Ouyang, Jun Lu, Georges El Fakhri, and Jonghye Woo. "Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/122.

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In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training. Considering the inherent conditional and label shifts, we would expect the alignment of p(x|y) and p(y). However, the widely used domain invariant feature learning (IFL) methods relies on aligning the marginal concept shift w.r.t. p(x), which rests on an unrealistic assumption that p(y) is invariant across domains. We thereby propose a novel variational Bayesian inference framework to enforce the conditional distribution alignment w.r.t. p(x|y) via the prior distribution matching in a latent space, which also takes the marginal label shift w.r.t. p(y) into consideration with the posterior alignment. Extensive experiments on various benchmarks demonstrate that our framework is robust to the label shift and the cross-domain accuracy is significantly improved, thereby achieving superior performance over the conventional IFL counterparts.
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Shi, Haochen, Siliang Tang, Xiaotao Gu, Bo Chen, Zhigang Chen, Jian Shao, and Xiang Ren. "Alleviate Dataset Shift Problem in Fine-grained Entity Typing with Virtual Adversarial Training." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/539.

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The recent success of Distant Supervision (DS) brings abundant labeled data for the task of fine-grained entity typing (FET) without human annotation. However, the heuristically generated labels inevitably bring a significant distribution gap, namely dataset shift, between the distantly labeled training set and the manually curated test set. Considerable efforts have been made to alleviate this problem from the label perspective by either intelligently denoising the training labels, or designing noise-aware loss functions. Despite their progress, the dataset shift can hardly be eliminated completely. In this work, complementary to the label perspective, we reconsider this problem from the model perspective: Can we learn a more robust typing model with the existence of dataset shift? To this end, we propose a novel regularization module based on virtual adversarial training (VAT). The proposed approach first uses a self-paced sample selection function to select suitable samples for VAT, then constructs virtual adversarial perturbations based on the selected samples, and finally regularizes the model to be robust to such perturbations. Experiments on two benchmarks demonstrate the effectiveness of the proposed method, with an average 3.8%, 2.5%, and 3.2% improvement in accuracy, Macro F1 and Micro F1 respectively compared to the next best method.
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Wen, Jun, Nenggan Zheng, Junsong Yuan, Zhefeng Gong, and Changyou Chen. "Bayesian Uncertainty Matching for Unsupervised Domain Adaptation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/534.

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Domain adaptation is an important technique to alleviate performance degradation caused by domain shift, e.g., when training and test data come from different domains. Most existing deep adaptation methods focus on reducing domain shift by matching marginal feature distributions through deep transformations on the input features, due to the unavailability of target domain labels. We show that domain shift may still exist via label distribution shift at the classifier, thus deteriorating model performances. To alleviate this issue, we propose an approximate joint distribution matching scheme by exploiting prediction uncertainty. Specifically, we use a Bayesian neural network to quantify prediction uncertainty of a classifier. By imposing distribution matching on both features and labels (via uncertainty), label distribution mismatching in source and target data is effectively alleviated, encouraging the classifier to produce consistent predictions across domains. We also propose a few techniques to improve our method by adaptively reweighting domain adaptation loss to achieve nontrivial distribution matching and stable training. Comparisons with state of the art unsupervised domain adaptation methods on three popular benchmark datasets demonstrate the superiority of our approach, especially on the effectiveness of alleviating negative transfer.
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Chow, C. W., and H. K. Tsang. "Optical packet labeling using polarization shift keying (PoISK) label and amplitude shift keying (ASK) payload." In 2005 Optical Fiber Communications Conference Technical Digest. IEEE, 2005. http://dx.doi.org/10.1109/ofc.2005.192564.

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Zhang, Qinming, Luyan Liu, Kai Ma, Cheng Zhuo, and Yefeng Zheng. "Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/146.

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Deep convolutional neural networks (DCNNs) have contributed many breakthroughs in segmentation tasks, especially in the field of medical imaging. However, domain shift and corrupted annotations, which are two common problems in medical imaging, dramatically degrade the performance of DCNNs in practice. In this paper, we propose a novel robust cross-denoising framework using two peer networks to address domain shift and corrupted label problems with a peer-review strategy. Specifically, each network performs as a mentor, mutually supervised to learn from reliable samples selected by the peer network to combat with corrupted labels. In addition, a noise-tolerant loss is proposed to encourage the network to capture the key location and filter the discrepancy under various noise-contaminant labels. To further reduce the accumulated error, we introduce a class-imbalanced cross learning using most confident predictions at class-level. Experimental results on REFUGE and Drishti-GS datasets for optic disc (OD) and optic cup (OC) segmentation demonstrate the superior performance of our proposed approach to the state-of-the-art methods.
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Liu, Yahao, Jinhong Deng, Jiale Tao, Tong Chu, Lixin Duan, and Wen Li. "Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.00691.

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Sordillo, Laura A., Peter P. Sordillo, and R. R. Alfano. "Label-free pathological evaluation of grade 3 cancer using Stokes shift spectroscopy." In SPIE BiOS, edited by Robert R. Alfano and Stavros G. Demos. SPIE, 2016. http://dx.doi.org/10.1117/12.2214327.

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Gawlikowski, Jakob, Sudipan Saha, Julia Niebling, and Xiao Xiang Zhu. "Robust Distribution-Shift Aware Sar-Optical data Fusion for Multi-Label Scene Classification." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884880.

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Rosenzweig, Julia, Eduardo Brito, Hans-Ulrich Kobialka, Maram Akila, Nico M. Schmidt, Peter Schlicht, Jan David Schneider, et al. "Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis." In 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops). IEEE, 2021. http://dx.doi.org/10.1109/ivworkshops54471.2021.9669248.

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Liu, Xiaofeng, Zhenhua Guo, Site Li, Fangxu Xing, Jane You, C. C. Jay Kuo, Georges El Fakhri, and Jonghye Woo. "Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.01020.

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