Дисертації з теми "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.
Повний текст джерелаRahman, 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.
Повний текст джерелаGustafsson, 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.
Повний текст джерелаBrandt, 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.
Повний текст джерелаMarzinotto, 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.
Повний текст джерелаMaking 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
Ackerman, Wesley. "Semantic-Driven Unsupervised Image-to-Image Translation for Distinct Image Domains." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8684.
Повний текст джерелаTsai, Jen-Chieh, and 蔡仁傑. "Deep Adversarial Learning and Domain Adaptation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3848u8.
Повний текст джерела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.
Wei, Kai-Ya, and 魏凱亞. "Generative Adversarial Guided Learning for Domain Adaptation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/zt2car.
Повний текст джерела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.
Pereira, João Afonso Pinto. "Fingerprint Anti Spoofing - Domain Adaptation and Adversarial Learning." Master's thesis, 2020. https://hdl.handle.net/10216/128390.
Повний текст джерелаPereira, João Afonso Pinto. "Fingerprint Anti Spoofing - Domain Adaptation and Adversarial Learning." Dissertação, 2020. https://hdl.handle.net/10216/128390.
Повний текст джерелаChen, Tseng-Hung, and 陳增鴻. "Generating Cross-domain Visual Description via Adversarial Learning." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/r8k45f.
Повний текст джерела國立清華大學
電機工程學系所
105
Impressive image captioning results are achieved in domains with plenty of training image and sentence pairs (e.g., MSCOCO). However, transferring to a target domain with significant domain shifts but no paired training data (referred to as cross-domain image captioning) remains largely unexplored. We propose a novel adversarial training procedure to leverage unpaired data in the target domain. Two critic networks are introduced to guide the captioner, namely domain critic and multi-modal critic. The domain critic assesses whether the generated sentences are indistinguishable from sentences in the target domain. The multi-modal critic assesses whether an image and its generated sentence are a valid pair. During training, the critics and captioner act as adversaries -- captioner aims to generate indistinguishable sentences, whereas critics aim at distinguishing them. The assessment improves the captioner through policy gradient updates. During inference, we further propose a novel critic-based planning method to select high-quality sentences without additional supervision (e.g., tags). To evaluate, we use MSCOCO as the source domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k) as the target domains. Our method consistently performs well on all datasets. Utilizing the learned critic during inference further boosts the overall performance in CUB-200 and Oxford-102. Furthermore, we extend our method to the task of video captioning. We observe improvements for the adaptation between large-scale video captioning datasets such as MSR-VTT, M-VAD and MPII-MD.
"Generalized Domain Adaptation for Visual Domains." Master's thesis, 2020. http://hdl.handle.net/2286/R.I.57226.
Повний текст джерелаDissertation/Thesis
Masters Thesis Computer Science 2020
Ganin, Iaroslav. "Natural image processing and synthesis using deep learning." Thèse, 2019. http://hdl.handle.net/1866/23437.
Повний текст джерелаIn the present thesis, we study how deep neural networks can be applied to various tasks in computer vision. Computer vision is an interdisciplinary field that deals with understanding of digital images and video. Traditionally, the problems arising in this domain were tackled using heavily hand-engineered adhoc methods. A typical computer vision system up until recently consisted of a sequence of independent modules which barely talked to each other. Such an approach is quite reasonable in the case of limited data as it takes major advantage of the researcher's domain expertise. This strength turns into a weakness if some of the input scenarios are overlooked in the algorithm design process. With the rapidly increasing volumes and varieties of data and the advent of cheaper and faster computational resources end-to-end deep neural networks have become an appealing alternative to the traditional computer vision pipelines. We demonstrate this in a series of research articles, each of which considers a particular task of either image analysis or synthesis and presenting a solution based on a ``deep'' backbone. In the first article, we deal with a classic low-level vision problem of edge detection. Inspired by a top-performing non-neural approach, we take a step towards building an end-to-end system by combining feature extraction and description in a single convolutional network. The resulting fully data-driven method matches or surpasses the detection quality of the existing conventional approaches in the settings for which they were designed while being significantly more usable in the out-of-domain situations. In our second article, we introduce a custom architecture for image manipulation based on the idea that most of the pixels in the output image can be directly copied from the input. This technique bears several significant advantages over the naive black-box neural approach. It retains the level of detail of the original images, does not introduce artifacts due to insufficient capacity of the underlying neural network and simplifies training process, to name a few. We demonstrate the efficiency of the proposed architecture on the challenging gaze correction task where our system achieves excellent results. In the third article, we slightly diverge from pure computer vision and study a more general problem of domain adaption. There, we introduce a novel training-time algorithm (\ie, adaptation is attained by using an auxilliary objective in addition to the main one). We seek to extract features that maximally confuse a dedicated network called domain classifier while being useful for the task at hand. The domain classifier is learned simultaneosly with the features and attempts to tell whether those features are coming from the source or the target domain. The proposed technique is easy to implement, yet results in superior performance in all the standard benchmarks. Finally, the fourth article presents a new kind of generative model for image data. Unlike conventional neural network based approaches our system dubbed SPIRAL describes images in terms of concise low-level programs executed by off-the-shelf rendering software used by humans to create visual content. Among other things, this allows SPIRAL not to waste its capacity on minutae of datasets and focus more on the global structure. The latent space of our model is easily interpretable by design and provides means for predictable image manipulation. We test our approach on several popular datasets and demonstrate its power and flexibility.
Serdyuk, Dmitriy. "Advances in deep learning methods for speech recognition and understanding." Thesis, 2020. http://hdl.handle.net/1866/24803.
Повний текст джерелаThis work presents several studies in the areas of speech recognition and understanding. The semantic speech understanding is an important sub-domain of the broader field of artificial intelligence. Speech processing has had interest from the researchers for long time because language is one of the defining characteristics of a human being. With the development of neural networks, the domain has seen rapid progress both in terms of accuracy and human perception. Another important milestone was achieved with the development of end-to-end approaches. Such approaches allow co-adaptation of all the parts of the model thus increasing the performance, as well as simplifying the training procedure. End-to-end models became feasible with the increasing amount of available data, computational resources, and most importantly with many novel architectural developments. Nevertheless, traditional, non end-to-end, approaches are still relevant for speech processing due to challenging data in noisy environments, accented speech, and high variety of dialects. In the first work, we explore the hybrid speech recognition in noisy environments. We propose to treat the recognition in the unseen noise condition as the domain adaptation task. For this, we use the novel at the time technique of the adversarial domain adaptation. In the nutshell, this prior work proposed to train features in such a way that they are discriminative for the primary task, but non-discriminative for the secondary task. This secondary task is constructed to be the domain recognition task. Thus, the features trained are invariant towards the domain at hand. In our work, we adopt this technique and modify it for the task of noisy speech recognition. In the second work, we develop a general method for regularizing the generative recurrent networks. It is known that the recurrent networks frequently have difficulties staying on same track when generating long outputs. While it is possible to use bi-directional networks for better sequence aggregation for feature learning, it is not applicable for the generative case. We developed a way improve the consistency of generating long sequences with recurrent networks. We propose a way to construct a model similar to bi-directional network. The key insight is to use a soft L2 loss between the forward and the backward generative recurrent networks. We provide experimental evaluation on a multitude of tasks and datasets, including speech recognition, image captioning, and language modeling. In the third paper, we investigate the possibility of developing an end-to-end intent recognizer for spoken language understanding. The semantic spoken language understanding is an important step towards developing a human-like artificial intelligence. We have seen that the end-to-end approaches show high performance on the tasks including machine translation and speech recognition. We draw the inspiration from the prior works to develop an end-to-end system for intent recognition.
Harkreader, Robert Chandler. "Playing Hide-and-Seek with Spammers: Detecting Evasive Adversaries in the Online Social Network Domain." Thesis, 2012. http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11479.
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