Relevant bibliographies by topics / Domain Adversarial Learning

Academic literature on the topic 'Domain Adversarial Learning'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Domain Adversarial Learning"

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Rosenberg, Ishai, Asaf Shabtai, Yuval Elovici, and Lior Rokach. "Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain." ACM Computing Surveys 54, no. 5 (June 2021): 1–36. http://dx.doi.org/10.1145/3453158.

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In recent years, machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this limits the application of machine learning, especially in non-stationary, adversarial environments, such as the cyber security domain, where actual adversaries (e.g., malware developers) exist. This article comprehensively summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques and illuminates the risks they pose. First, the adversarial attack methods are characterized based on their stage of occurrence, and the attacker’ s goals and capabilities. Then, we categorize the applications of adversarial attack and defense methods in the cyber security domain. Finally, we highlight some characteristics identified in recent research and discuss the impact of recent advancements in other adversarial learning domains on future research directions in the cyber security domain. To the best of our knowledge, this work is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain, map them in a unified taxonomy, and use the taxonomy to highlight future research directions.
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Xu, Minghao, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang. "Adversarial Domain Adaptation with Domain Mixup." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6502–9. http://dx.doi.org/10.1609/aaai.v34i04.6123.

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Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exist in current adversarial-learning-based methods. First, samples from two domains alone are not sufficient to ensure domain-invariance at most part of latent space. Second, the domain discriminator involved in these methods can only judge real or fake with the guidance of hard label, while it is more reasonable to use soft scores to evaluate the generated images or features, i.e., to fully utilize the inter-domain information. In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains. Domain mixup is jointly conducted on pixel and feature level to improve the robustness of models. Extensive experiments prove that the proposed approach can achieve superior performance on tasks with various degrees of domain shift and data complexity.
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Wu, Lan, Chongyang Li, Qiliang Chen, and Binquan Li. "Deep adversarial domain adaptation network." International Journal of Advanced Robotic Systems 17, no. 5 (September 1, 2020): 172988142096464. http://dx.doi.org/10.1177/1729881420964648.

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The advantage of adversarial domain adaptation is that it uses the idea of adversarial adaptation to confuse the feature distribution of two domains and solve the problem of domain transfer in transfer learning. However, although the discriminator completely confuses the two domains, adversarial domain adaptation still cannot guarantee the consistent feature distribution of the two domains, which may further deteriorate the recognition accuracy. Therefore, in this article, we propose a deep adversarial domain adaptation network, which optimises the feature distribution of the two confused domains by adding multi-kernel maximum mean discrepancy to the feature layer and designing a new loss function to ensure good recognition accuracy. In the last part, some simulation results based on the Office-31 and Underwater data sets show that the deep adversarial domain adaptation network can optimise the feature distribution and promote positive transfer, thus improving the classification accuracy.
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Zhou, Kaiyang, Yongxin Yang, Timothy Hospedales, and Tao Xiang. "Deep Domain-Adversarial Image Generation for Domain Generalisation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 13025–32. http://dx.doi.org/10.1609/aaai.v34i07.7003.

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Machine learning models typically suffer from the domain shift problem when trained on a source dataset and evaluated on a target dataset of different distribution. To overcome this problem, domain generalisation (DG) methods aim to leverage data from multiple source domains so that a trained model can generalise to unseen domains. In this paper, we propose a novel DG approach based on Deep Domain-Adversarial Image Generation (DDAIG). Specifically, DDAIG consists of three components, namely a label classifier, a domain classifier and a domain transformation network (DoTNet). The goal for DoTNet is to map the source training data to unseen domains. This is achieved by having a learning objective formulated to ensure that the generated data can be correctly classified by the label classifier while fooling the domain classifier. By augmenting the source training data with the generated unseen domain data, we can make the label classifier more robust to unknown domain changes. Extensive experiments on four DG datasets demonstrate the effectiveness of our approach.
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Chen, Minghao, Shuai Zhao, Haifeng Liu, and Deng Cai. "Adversarial-Learned Loss for Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3521–28. http://dx.doi.org/10.1609/aaai.v34i04.5757.

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Recently, remarkable progress has been made in learning transferable representation across domains. Previous works in domain adaptation are majorly based on two techniques: domain-adversarial learning and self-training. However, domain-adversarial learning only aligns feature distributions between domains but does not consider whether the target features are discriminative. On the other hand, self-training utilizes the model predictions to enhance the discrimination of target features, but it is unable to explicitly align domain distributions. In order to combine the strengths of these two methods, we propose a novel method called Adversarial-Learned Loss for Domain Adaptation (ALDA). We first analyze the pseudo-label method, a typical self-training method. Nevertheless, there is a gap between pseudo-labels and the ground truth, which can cause incorrect training. Thus we introduce the confusion matrix, which is learned through an adversarial manner in ALDA, to reduce the gap and align the feature distributions. Finally, a new loss function is auto-constructed from the learned confusion matrix, which serves as the loss for unlabeled target samples. Our ALDA outperforms state-of-the-art approaches in four standard domain adaptation datasets. Our code is available at https://github.com/ZJULearning/ALDA.
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Wu, Yuan, and Yuhong Guo. "Dual Adversarial Co-Learning for Multi-Domain Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6438–45. http://dx.doi.org/10.1609/aaai.v34i04.6115.

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With the advent of deep learning, the performance of text classification models have been improved significantly. Nevertheless, the successful training of a good classification model requires a sufficient amount of labeled data, while it is always expensive and time consuming to annotate data. With the rapid growth of digital data, similar classification tasks can typically occur in multiple domains, while the availability of labeled data can largely vary across domains. Some domains may have abundant labeled data, while in some other domains there may only exist a limited amount (or none) of labeled data. Meanwhile text classification tasks are highly domain-dependent — a text classifier trained in one domain may not perform well in another domain. In order to address these issues, in this paper we propose a novel dual adversarial co-learning approach for multi-domain text classification (MDTC). The approach learns shared-private networks for feature extraction and deploys dual adversarial regularizations to align features across different domains and between labeled and unlabeled data simultaneously under a discrepancy based co-learning framework, aiming to improve the classifiers' generalization capacity with the learned features. We conduct experiments on multi-domain sentiment classification datasets. The results show the proposed approach achieves the state-of-the-art MDTC performance.
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Zou, Han, Yuxun Zhou, Jianfei Yang, Huihan Liu, Hari Prasanna Das, and Costas J. Spanos. "Consensus Adversarial Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5997–6004. http://dx.doi.org/10.1609/aaai.v33i01.33015997.

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We propose a novel domain adaptation framework, namely Consensus Adversarial Domain Adaptation (CADA), that gives freedom to both target encoder and source encoder to embed data from both domains into a common domaininvariant feature space until they achieve consensus during adversarial learning. In this manner, the domain discrepancy can be further minimized in the embedded space, yielding more generalizable representations. The framework is also extended to establish a new few-shot domain adaptation scheme (F-CADA), that remarkably enhances the ADA performance by efficiently propagating a few labeled data once available in the target domain. Extensive experiments are conducted on the task of digit recognition across multiple benchmark datasets and a real-world problem involving WiFi-enabled device-free gesture recognition under spatial dynamics. The results show the compelling performance of CADA versus the state-of-the-art unsupervised domain adaptation (UDA) and supervised domain adaptation (SDA) methods. Numerical experiments also demonstrate that F-CADA can significantly improve the adaptation performance even with sparsely labeled data in the target domain.
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Tang, Hui, and Kui Jia. "Discriminative Adversarial Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5940–47. http://dx.doi.org/10.1609/aaai.v34i04.6054.

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Given labeled instances on a source domain and unlabeled ones on a target domain, unsupervised domain adaptation aims to learn a task classifier that can well classify target instances. Recent advances rely on domain-adversarial training of deep networks to learn domain-invariant features. However, due to an issue of mode collapse induced by the separate design of task and domain classifiers, these methods are limited in aligning the joint distributions of feature and category across domains. To overcome it, we propose a novel adversarial learning method termed Discriminative Adversarial Domain Adaptation (DADA). Based on an integrated category and domain classifier, DADA has a novel adversarial objective that encourages a mutually inhibitory relation between category and domain predictions for any input instance. We show that under practical conditions, it defines a minimax game that can promote the joint distribution alignment. Except for the traditional closed set domain adaptation, we also extend DADA for extremely challenging problem settings of partial and open set domain adaptation. Experiments show the efficacy of our proposed methods and we achieve the new state of the art for all the three settings on benchmark datasets.
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Li, Wenjing, and Zhongcheng Wu. "OVL: One-View Learning for Human Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11410–17. http://dx.doi.org/10.1609/aaai.v34i07.6804.

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This paper considers a novel problem, named One-View Learning (OVL), in human retrieval a.k.a. person re-identification (re-ID). Unlike fully-supervised learning, OVL only requires pretty cheap annotation cost: labeled training images are only provided from one camera view (source view/domain), while the annotations of training images from other camera views (target views/domains) are not available. OVL is a problem of multi-target open set domain adaptation that is difficult for existing domain adaptation methods to handle. This is because 1) unlabeled samples are drawn from multiple target views in different distributions, and 2) the target views may contain samples of “unknown identity” that are not shared by the source view. To address this problem, this work introduces a novel one-view learning framework for person re-ID. This is achieved by adversarial multi-view learning (AMVL) and adversarial unknown rejection learning (AURL). The former learns a multi-view discriminator by adversarial learning to align the feature distributions between all views. The later is designed to reject unknown samples from target views through adversarial learning with two unknown identity classifiers. Extensive experiments on three large-scale datasets demonstrate the advantage of the proposed method over state-of-the-art domain adaptation and semi-supervised methods.
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Nguyen Duc, Tho, Chanh Minh Tran, Phan Xuan Tan, and Eiji Kamioka. "Domain Adaptation for Imitation Learning Using Generative Adversarial Network." Sensors 21, no. 14 (July 9, 2021): 4718. http://dx.doi.org/10.3390/s21144718.

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Imitation learning is an effective approach for an autonomous agent to learn control policies when an explicit reward function is unavailable, using demonstrations provided from an expert. However, standard imitation learning methods assume that the agents and the demonstrations provided by the expert are in the same domain configuration. Such an assumption has made the learned policies difficult to apply in another distinct domain. The problem is formalized as domain adaptive imitation learning, which is the process of learning how to perform a task optimally in a learner domain, given demonstrations of the task in a distinct expert domain. We address the problem by proposing a model based on Generative Adversarial Network. The model aims to learn both domain-shared and domain-specific features and utilizes it to find an optimal policy across domains. The experimental results show the effectiveness of our model in a number of tasks ranging from low to complex high-dimensional.
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Dissertations / Theses on the topic "Domain Adversarial Learning"

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Bejiga, Mesay Belete. "Adversarial approaches to remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257100.

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The recent advance in generative modeling in particular the unsupervised learning of data distribution is attributed to the invention of models with new learning algorithms. Among the methods proposed, generative adversarial networks (GANs) have shown to be the most efficient approaches to estimate data distributions. The core idea of GANs is an adversarial training of two deep neural networks, called generator and discriminator, to learn an implicit approximation of the true data distribution. The distribution is approximated through the weights of the generator network, and interaction with the distribution is through the process of sampling. GANs have found to be useful in applications such as image-to-image translation, in-painting, and text-to-image synthesis. In this thesis, we propose to capitalize on the power of GANs for different remote sensing problems. The first problem is a new research track to the remote sensing community that aims to generate remote sensing images from text descriptions. More specifically, we focus on exploiting ancient text descriptions of geographical areas, inherited from previous civilizations, and convert them the equivalent remote sensing images. The proposed method is composed of a text encoder and an image synthesis module. The text encoder is tasked with converting a text description into a vector. To this end, we explore two encoding schemes: a multilabel encoder and a doc2vec encoder. The multilabel encoder takes into account the presence or absence of objects in the encoding process whereas the doc2vec method encodes additional information available in the text. The encoded vectors are then used as conditional information to a GAN network and guide the synthesis process. We collected satellite images and ancient text descriptions for training in order to evaluate the efficacy of the proposed method. The qualitative and quantitative results obtained suggest that the doc2vec encoder-based model yields better images in terms of the semantic agreement with the input description. In addition, we present open research areas that we believe are important to further advance this new research area. The second problem we want to address is the issue of semi-supervised domain adaptation. The goal of domain adaptation is to learn a generic classifier for multiple related problems, thereby reducing the cost of labeling. To that end, we propose two methods. The first method uses GANs in the context of image-to-image translation to adapt source domain images into target domain images and train a classifier using the adapted images. We evaluated the proposed method on two remote sensing datasets. Though we have not explored this avenue extensively due to computational challenges, the results obtained show that the proposed method is promising and worth exploring in the future. The second domain adaptation strategy borrows the adversarial property of GANs to learn a new representation space where the domain discrepancy is negligible, and the new features are discriminative enough. The method is composed of a feature extractor, class predictor, and domain classifier blocks. Contrary to the traditional methods that perform representation and classifier learning in separate stages, this method combines both into a single-stage thereby learning a new representation of the input data that is domain invariant and discriminative. After training, the classifier is used to predict both source and target domain labels. We apply this method for large-scale land cover classification and cross-sensor hyperspectral classification problems. Experimental results obtained show that the proposed method provides a performance gain of up to 40%, and thus indicates the efficacy of the method.
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Rahman, Mohammad Mahfujur. "Deep domain adaptation and generalisation." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205619/1/Mohammad%20Mahfujur_Rahman_Thesis.pdf.

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This thesis addresses a critical problem in computer vision of dealing with dataset bias between source and target environments. Variations in image data can arise from multiple factors including contrasts in picture quality (shading, brightness, colour, resolution, and occlusion), diverse backgrounds, distinct circumstances, changes in camera viewpoint, and implicit heterogeneity of the samples themselves. This research developed strategies to address this domain shift problem for the object recognition task. Several domain adaptation and generalization approaches based on deep neural networks were introduced to improve poor performance due to domain shift or domain bias.
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Gustafsson, Fredrik, and Erik Linder-Norén. "Automotive 3D Object Detection Without Target Domain Annotations." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148585.

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In this thesis we study a perception problem in the context of autonomous driving. Specifically, we study the computer vision problem of 3D object detection, in which objects should be detected from various sensor data and their position in the 3D world should be estimated. We also study the application of Generative Adversarial Networks in domain adaptation techniques, aiming to improve the 3D object detection model's ability to transfer between different domains. The state-of-the-art Frustum-PointNet architecture for LiDAR-based 3D object detection was implemented and found to closely match its reported performance when trained and evaluated on the KITTI dataset. The architecture was also found to transfer reasonably well from the synthetic SYN dataset to KITTI, and is thus believed to be usable in a semi-automatic 3D bounding box annotation process. The Frustum-PointNet architecture was also extended to explicitly utilize image features, which surprisingly degraded its detection performance. Furthermore, an image-only 3D object detection model was designed and implemented, which was found to compare quite favourably with current state-of-the-art in terms of detection performance. Additionally, the PixelDA approach was adopted and successfully applied to the MNIST to MNIST-M domain adaptation problem, which validated the idea that unsupervised domain adaptation using Generative Adversarial Networks can improve the performance of a task network for a dataset lacking ground truth annotations. Surprisingly, the approach did however not significantly improve upon the performance of the image-based 3D object detection models when trained on the SYN dataset and evaluated on KITTI.
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Brandt, Carl-Simon, Jonathan Kleivard, and Andreas Turesson. "Convolutional, adversarial and random forest-based DGA detection : Comparative study for DGA detection with different machine learning algorithms." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20103.

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Malware is becoming more intelligent as static methods for blocking communication with Command and Control (C&C) server are becoming obsolete. Domain Generation Algorithms (DGAs) are a common evasion technique that generates pseudo-random domain names to communicate with C&C servers in a difficult way to detect using handcrafted methods. Trying to detect DGAs by looking at the domain name is a broad and efficient approach to detect malware-infected hosts. This gives us the possibility of detecting a wider assortment of malware compared to other techniques, even without knowledge of the malware’s existence. Our study compared the effectiveness of three different machine learning classifiers: Convolutional Neural Network (CNN), Generative Adversarial Network (GAN) and Random Forest (RF) when recognizing patterns and identifying these pseudo-random domains. The result indicates that CNN differed significantly from GAN and RF. It achieved 97.46% accuracy in the final evaluation, while RF achieved 93.89% and GAN achieved 60.39%. In the future, network traffic (efficiency) could be a key component to examine, as productivity may be harmed if the networkis over burdened by domain identification using machine learning algorithms.
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Marzinotto, Gabriel. "Semantic frame based analysis using machine learning techniques : improving the cross-domain generalization of semantic parsers." Electronic Thesis or Diss., Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0483.

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Rendre les analyseurs sémantiques robustes aux variations lexicales et stylistiques est un véritable défi pour de nombreuses applications industrielles. De nos jours, l'analyse sémantique nécessite de corpus annotés spécifiques à chaque domaine afin de garantir des performances acceptables. Les techniques d'apprenti-ssage par transfert sont largement étudiées et adoptées pour résoudre ce problème de manque de robustesse et la stratégie la plus courante consiste à utiliser des représentations de mots pré-formés. Cependant, les meilleurs analyseurs montrent toujours une dégradation significative des performances lors d'un changement de domaine, mettant en évidence la nécessité de stratégies d'apprentissage par transfert supplémentaires pour atteindre la robustesse. Ce travail propose une nouvelle référence pour étudier le problème de dépendance de domaine dans l'analyse sémantique. Nous utilisons un nouveau corpus annoté pour évaluer les techniques classiques d'apprentissage par transfert et pour proposer et évaluer de nouvelles techniques basées sur les réseaux antagonistes. Toutes ces techniques sont testées sur des analyseurs sémantiques de pointe. Nous affirmons que les approches basées sur les réseaux antagonistes peuvent améliorer les capacités de généralisation des modèles. Nous testons cette hypothèse sur différents schémas de représentation sémantique, langages et corpus, en fournissant des résultats expérimentaux à l'appui de notre hypothèse
Making semantic parsers robust to lexical and stylistic variations is a real challenge with many industrial applications. Nowadays, semantic parsing requires the usage of domain-specific training corpora to ensure acceptable performances on a given domain. Transfer learning techniques are widely studied and adopted when addressing this lack of robustness, and the most common strategy is the usage of pre-trained word representations. However, the best parsers still show significant performance degradation under domain shift, evidencing the need for supplementary transfer learning strategies to achieve robustness. This work proposes a new benchmark to study the domain dependence problem in semantic parsing. We use this bench to evaluate classical transfer learning techniques and to propose and evaluate new techniques based on adversarial learning. All these techniques are tested on state-of-the-art semantic parsers. We claim that adversarial learning approaches can improve the generalization capacities of models. We test this hypothesis on different semantic representation schemes, languages and corpora, providing experimental results to support our hypothesis
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Ackerman, Wesley. "Semantic-Driven Unsupervised Image-to-Image Translation for Distinct Image Domains." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8684.

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We expand the scope of image-to-image translation to include more distinct image domains, where the image sets have analogous structures, but may not share object types between them. Semantic-Driven Unsupervised Image-to-Image Translation for Distinct Image Domains (SUNIT) is built to more successfully translate images in this setting, where content from one domain is not found in the other. Our method trains an image translation model by learning encodings for semantic segmentations of images. These segmentations are translated between image domains to learn meaningful mappings between the structures in the two domains. The translated segmentations are then used as the basis for image generation. Beginning image generation with encoded segmentation information helps maintain the original structure of the image. We qualitatively and quantitatively show that SUNIT improves image translation outcomes, especially for image translation tasks where the image domains are very distinct.
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Tsai, Jen-Chieh, and 蔡仁傑. "Deep Adversarial Learning and Domain Adaptation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3848u8.

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碩士
國立交通大學
電機工程學系
105
Deep learning has been rapidly developing from different aspects of theories and applications where a large amount of labeled data are available for supervised training. However, in practice, it is time-consuming to collect a large set of labeled data. In real world, we may only observe a limited set of labeled data and unlabeled data. How to perform data augmentation and improve model regularization is a crucial research topic. Recently, adversarial learning has been discovering to generate or synthesize realistic data without the mixing problem in traditional model based on Markov chain. This thesis deals with the generation of new training samples based on deep adversarial learning. Our goal is to carry out the adversarial generation of new samples and apply it for defect classification in manufacturing process. To improve system performance, we introduce the additional latent codes and maximize the mutual information between generative samples and latent codes to build a conditional generative adversarial model. This model is capable of generating a variety of samples under the same class. We evaluate the performance of this unsupervised model by detecting the defect conditions in production process of copper foil images. On the other hand, transfer learning provides an alternative method to handle the problem of insufficient labeled data where data generation is not required. Transfer learning involves several issues owing to different setups. The issue we concern is mainly on domain adaptation. Domain adaptation aims to adapt a model from source domain to target domain through learning the shared representation that allows knowledge transfer across domains. Traditional domain adaptation methods are specialized to learn the shared representation for distribution matching between source domain and target domain where the individual information in both domains is missing. In this thesis, we present a deep hybrid adversarial learning framework which captures the shared information and the individual information simultaneously. Our idea is to estimate the shared feature which is informative for classification and the individual feature which contains the domain specific information. We use adaptation network to extract the shared feature and separation network to extract individual feature. Both adaptation and separation network are seen as an adversarial network. A hybrid adversarial learning is incorporated in the separation network as well as the adaptation network where the according to the minimax optimization over separation loss and domain discrepancy, respectively. The idea in the adaptation network is that we want to extract shared feature that an optimal discriminator cannot tell where feature come from. The idea in the separation network is that we want to extract feature including shared and individual feature which can be separated even by a bad discriminator. In other words the features have to be good enough to force the discriminator to classify them correctly. For the experiment on generative adversarial model, we investigate different unsupervised learning methods for defect detection in presence of copper foil images. In general, defect detection requires very high accuracy but the defect rate usually is relatively low which means the images with and without defect are very unbalanced. We generate the defective images to balance the training data between defective images and non-defective images conditioned on different classes. For the experiments on domain adaptation problem, we evaluate the proposed method on different tasks and show the merit of using the proposed adversarial domain separation and adaptation in the tasks of sentiment classification and image recognition.
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Wei, Kai-Ya, and 魏凱亞. "Generative Adversarial Guided Learning for Domain Adaptation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/zt2car.

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碩士
國立清華大學
資訊工程學系所
106
This thesis focuses on unsupervised domain adaptation problem, which aims to learn a classification model on an unlabelled target domain by referring to a fully-labelled source domain. Our goal is twofold: bridging the gap between source-target domains, and deriving a discriminative model for the target domain. We propose a Generative Adversarial Guided Learning (GAGL) model to tackle the task. To minimize the source-target domain shift, we adopt the idea of domain adversarial training to build a classification network. Next, to derive a target discriminative classifier, we propose to include a generative network to guide the classifier so as to push its decision boundaries away from high density area of target domain. The proposed GAGL model is an end-to-end framework and thus can simultaneously learn the classification model and refine its decision boundary under the guidance of the generator. Our experimental results show that the proposed GAGL model not only outperforms the baseline domain adversarial model but also achieves competitive results with state-of-the-art methods on standard benchmarks.
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Pereira, João Afonso Pinto. "Fingerprint Anti Spoofing - Domain Adaptation and Adversarial Learning." Master's thesis, 2020. https://hdl.handle.net/10216/128390.

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Pereira, João Afonso Pinto. "Fingerprint Anti Spoofing - Domain Adaptation and Adversarial Learning." Dissertação, 2020. https://hdl.handle.net/10216/128390.

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Book chapters on the topic "Domain Adversarial Learning"

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Lu, Weikai, Jian Chen, Hao Zheng, Haoyi Fan, Eng Yee Wei, Xinrong Cao, and Deyang Zhang. "Domain Adversarial Interaction Network for Cross-Domain Fault Diagnosis." In Machine Learning for Cyber Security, 436–46. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20099-1_37.

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Chen, Qian, Yuntao Du, Zhiwen Tan, Yi Zhang, and Chongjun Wang. "Unsupervised Domain Adaptation with Joint Domain-Adversarial Reconstruction Networks." In Machine Learning and Knowledge Discovery in Databases, 640–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67661-2_38.

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Guo, Zuwei, Nahid UI Islam, Michael B. Gotway, and Jianming Liang. "Discriminative, Restorative, and Adversarial Learning: Stepwise Incremental Pretraining." In Domain Adaptation and Representation Transfer, 66–76. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16852-9_7.

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Wang, Jinghua, and Jianmin Jiang. "Adversarial Learning for Zero-Shot Domain Adaptation." In Computer Vision – ECCV 2020, 329–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58589-1_20.

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Zhang, Youshan, and Brian D. Davison. "Adversarial Continuous Learning in Unsupervised Domain Adaptation." In Pattern Recognition. ICPR International Workshops and Challenges, 672–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68790-8_52.

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Saito, Kuniaki, Shohei Yamamoto, Yoshitaka Ushiku, and Tatsuya Harada. "Adversarial Learning Approach for Open Set Domain Adaptation." In Domain Adaptation in Computer Vision with Deep Learning, 175–93. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45529-3_10.

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Hu, Lanqing, Meina Kan, Shiguang Shan, and Xilin Chen. "Unsupervised Domain Adaptation with Duplex Generative Adversarial Network." In Domain Adaptation in Computer Vision with Deep Learning, 97–116. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45529-3_6.

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Wu, Yuan, Diana Inkpen, and Ahmed El-Roby. "Dual Mixup Regularized Learning for Adversarial Domain Adaptation." In Computer Vision – ECCV 2020, 540–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58526-6_32.

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Memmel, Marius, Camila Gonzalez, and Anirban Mukhopadhyay. "Adversarial Continual Learning for Multi-domain Hippocampal Segmentation." In Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health, 35–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87722-4_4.

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Grießhaber, Daniel, Ngoc Thang Vu, and Johannes Maucher. "Low-Resource Text Classification Using Domain-Adversarial Learning." In Statistical Language and Speech Processing, 129–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00810-9_12.

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Conference papers on the topic "Domain Adversarial Learning"

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Zhang, Zhifeng, Xuejing Kang, and Anlong Ming. "Domain Adversarial Learning for Color Constancy." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/236.

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Color Constancy aims to eliminate the color cast of RAW images caused by non-neutral illuminants. Though contemporary approaches based on convolutional neural networks significantly improve illuminant estimation, they suffer from the seriously insufficient data problem. To solve this problem by effectively utilizing multi-domain data, we propose the Domain Adversarial Learning Color Constancy (DALCC) which consists of the Domain Adversarial Learning Branch (DALB) and the Feature Reweighting Module (FRM). In DALB, the Camera Domain Classifier and the feature extractor compete against each other in an adversarial way to encourage the emergence of domain-invariant features. At the same time, the Illuminant Transformation Module performs color space conversion to solve the inconsistent color space problem caused by those domain-invariant features. They collaboratively avoid model degradation of multi-device training caused by the domain discrepancy of feature distribution, which enables our DALCC to benefit from multi-domain data. Besides, to better utilize multi-domain data, we propose the FRM that reweights the feature map to suppress Non-Primary Illuminant regions, which reduces the influence of misleading illuminant information. Experiments show that the proposed DALCC can more effectively take advantage of multi-domain data and thus achieve state-of-the-art performance on commonly used benchmark datasets.
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Raab, Christoph, Sascha Saralajew, and Frank-Michael Schleif. "Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation." In ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com, 2021. http://dx.doi.org/10.14428/esann/2021.es2021-103.

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Chien, Jen-Tzung, and Ching-Wei Huang. "Stochastic Adversarial Learning for Domain Adaptation." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207478.

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Li, Haoliang, Sinno Jialin Pan, Shiqi Wang, and Alex C. Kot. "Domain Generalization with Adversarial Feature Learning." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00566.

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Tsai, Jen-Chieh, and Jen-Tzung Chien. "Adversarial domain separation and adaptation." In 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2017. http://dx.doi.org/10.1109/mlsp.2017.8168121.

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Jiang, Pin, Aming Wu, Yahong Han, Yunfeng Shao, Meiyu Qi, and Bingshuai Li. "Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation." 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.2020/130.

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Semi-supervised domain adaptation (SSDA) is a novel branch of machine learning that scarce labeled target examples are available, compared with unsupervised domain adaptation. To make effective use of these additional data so as to bridge the domain gap, one possible way is to generate adversarial examples, which are images with additional perturbations, between the two domains and fill the domain gap. Adversarial training has been proven to be a powerful method for this purpose. However, the traditional adversarial training adds noises in arbitrary directions, which is inefficient to migrate between domains, or generate directional noises from the source to target domain and reverse. In this work, we devise a general bidirectional adversarial training method and employ gradient to guide adversarial examples across the domain gap, i.e., the Adaptive Adversarial Training (AAT) for source to target domain and Entropy-penalized Virtual Adversarial Training (E-VAT) for target to source domain. Particularly, we devise a Bidirectional Adversarial Training (BiAT) network to perform diverse adversarial trainings jointly. We evaluate the effectiveness of BiAT on three benchmark datasets and experimental results demonstrate the proposed method achieves the state-of-the-art.
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Osahor, Uche, and Nasser Nasrabadi. "Deep adversarial attack on target detection systems." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, edited by Tien Pham. SPIE, 2019. http://dx.doi.org/10.1117/12.2518970.

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Yang, Pei, Qi Tan, Jieping Ye, Hanghang Tong, and Jingrui He. "Deep Multi-Task Learning with Adversarial-and-Cooperative Nets." 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.2019/566.

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In this paper, we propose a deep multi-Task learning model based on Adversarial-and-COoperative nets (TACO). The goal is to use an adversarial-and-cooperative strategy to decouple the task-common and task-specific knowledge, facilitating the fine-grained knowledge sharing among tasks. TACO accommodates multiple game players, i.e., feature extractors, domain discriminator, and tri-classifiers. They play the MinMax games adversarially and cooperatively to distill the task-common and task-specific features, while respecting their discriminative structures. Moreover, it adopts a divide-and-combine strategy to leverage the decoupled multi-view information to further improve the generalization performance of the model. The experimental results show that our proposed method significantly outperforms the state-of-the-art algorithms on the benchmark datasets in both multi-task learning and semi-supervised domain adaptation scenarios.
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Sinatra, Taylor, Cristina Comaniciu, Myron Hohil, and Thomas A. LaPeruta. "Cooperative monitoring for detecting adversarial communication." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, edited by Tien Pham, Latasha Solomon, and Myron E. Hohil. SPIE, 2021. http://dx.doi.org/10.1117/12.2588038.

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You, Suya, and C.-C. Jay Kuo. "Defending against adversarial attacks in deep neural networks." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, edited by Tien Pham. SPIE, 2019. http://dx.doi.org/10.1117/12.2519268.

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