scholarly journals Confidence Measures for Deep Learning in Domain Adaptation

2019 ◽  
Vol 9 (11) ◽  
pp. 2192 ◽  
Author(s):  
Simone Bonechi ◽  
Paolo Andreini ◽  
Monica Bianchini ◽  
Akshay Pai ◽  
Franco Scarselli
Keyword(s):  
Domain Adaptation ◽  
Medical Image Analysis ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Huge Amount ◽  
Confidence Measures ◽  
Adversarial Networks ◽  
Large Sets ◽  
Learning Tasks ◽  
Network Output

In recent years, Deep Neural Networks (DNNs) have led to impressive results in a wide variety of machine learning tasks, typically relying on the existence of a huge amount of supervised data. However, in many applications (e.g., bio–medical image analysis), gathering large sets of labeled data can be very difficult and costly. Unsupervised domain adaptation exploits data from a source domain, where annotations are available, to train a model able to generalize also to a target domain, where labels are unavailable. Recent research has shown that Generative Adversarial Networks (GANs) can be successfully employed for domain adaptation, although deciding when to stop learning is a major concern for GANs. In this work, we propose some confidence measures that can be used to early stop the GAN training, also showing how such measures can be employed to predict the reliability of the network output. The effectiveness of the proposed approach has been tested in two domain adaptation tasks, with very promising results.

scholarly journals Unsupervised Domain Adaptation Using Generative Adversarial Networks for Semantic Segmentation of Aerial Images

2019 ◽  
Vol 11 (11) ◽  
pp. 1369 ◽  
Author(s):  
Bilel Benjdira ◽  
Yakoub Bazi ◽  
Anis Koubaa ◽  
Kais Ouni
Keyword(s):  
Urban Areas ◽  
Traffic Management ◽  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Adversarial Networks ◽  
Segmentation Accuracy ◽  
New City

Segmenting aerial images is of great potential in surveillance and scene understanding of urban areas. It provides a mean for automatic reporting of the different events that happen in inhabited areas. This remarkably promotes public safety and traffic management applications. After the wide adoption of convolutional neural networks methods, the accuracy of semantic segmentation algorithms could easily surpass 80% if a robust dataset is provided. Despite this success, the deployment of a pretrained segmentation model to survey a new city that is not included in the training set significantly decreases accuracy. This is due to the domain shift between the source dataset on which the model is trained and the new target domain of the new city images. In this paper, we address this issue and consider the challenge of domain adaptation in semantic segmentation of aerial images. We designed an algorithm that reduces the domain shift impact using generative adversarial networks (GANs). In the experiments, we tested the proposed methodology on the International Society for Photogrammetry and Remote Sensing (ISPRS) semantic segmentation dataset and found that our method improves overall accuracy from 35% to 52% when passing from the Potsdam domain (considered as source domain) to the Vaihingen domain (considered as target domain). In addition, the method allows efficiently recovering the inverted classes due to sensor variation. In particular, it improves the average segmentation accuracy of the inverted classes due to sensor variation from 14% to 61%.

scholarly journals Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks

2018 ◽  
Author(s):  
Feiwu Yu ◽  
Xinxiao Wu ◽  
Yuchao Sun ◽  
Lixin Duan
Keyword(s):  
Domain Adaptation ◽  
Feature Representation ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Feature Representations ◽  
Adversarial Networks ◽  
Video Recognition ◽  
Novel Method ◽  
High Level ◽  
Heterogeneous Feature

Existing deep learning methods of video recognition usually require a large number of labeled videos for training. But for a new task, videos are often unlabeled and it is also time-consuming and labor-intensive to annotate them. Instead of human annotation, we try to make use of existing fully labeled images to help recognize those videos. However, due to the problem of domain shifts and heterogeneous feature representations, the performance of classifiers trained on images may be dramatically degraded for video recognition tasks. In this paper, we propose a novel method, called Hierarchical Generative Adversarial Networks (HiGAN), to enhance recognition in videos (i.e., target domain) by transferring knowledge from images (i.e., source domain). The HiGAN model consists of a \emph{low-level} conditional GAN and a \emph{high-level} conditional GAN. By taking advantage of these two-level adversarial learning, our method is capable of learning a domain-invariant feature representation of source images and target videos. Comprehensive experiments on two challenging video recognition datasets (i.e. UCF101 and HMDB51) demonstrate the effectiveness of the proposed method when compared with the existing state-of-the-art domain adaptation methods.

scholarly journals Cycle-consistent Generative Adversarial Networks (CycleGANs) for the Non-Parallel Creation of Fake Voice Media

2019 ◽  
Author(s):  
Daniel Fleury ◽  
Angelica Fleury
Keyword(s):  
Domain Adaptation ◽  
Discrete Source ◽  
Voice Conversion ◽  
Generative Adversarial Networks ◽  
Male Voice ◽  
Target Domain ◽  
Ethical Implications ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Object Features

The upsurge of Generative Adversarial Networks (GANs) in the previous five years has led to advancements in unsupervised data manipulation, sourced feature translation, and precise input-output synthesis through a competitive optimization of the discriminator and generator networks. More specifically, the recent rise of cycle-consistent GANs enables style transfers from a discrete source (input A) to target domain (input B) by preprocessing object features for a multi-discriminative adversarial network. Traditionally, cyclical adversarial networks have been exploited for unpaired image-to-image translation and domain adaptation by determining mapped relationships between an input A graphic and an input B graphic. However, this integral mechanism of domain adaptation can be applied to the complex acoustical features of human speech. Although well-established datasets, such as the 2018 Voice Conversion Challenge repository, paved way for female-male voice transformation, cycle-GANs have rarely been re-engineered for voices outside the datasets. More critically, cycle-GANs have massive potential to extract surface-level and hidden feature to distort an input A source into a texturally unrelated target voice. By preprocessing, compressing, and packaging unique acoustical voice properties, CycleGANs can learn to decompose speech signals and implement new translation models while preserving emotion, the intent of words, rhythm, and accents. Due to the potential of CycleGAN’s autoencoder in realistic unsupervised voice-voice conversion/feature adaptation, the researchers raise the ethical implications of controlling source input A to manipulate target voice B, particularly in cases of defamation and sabotage of target B’s words. This paper analyzes the potential of cycle-consistent GANs in deceptive voice-voice conversion by manipulating interview excerpts of political candidates.

scholarly journals Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks

2020 ◽  
Vol 10 (3) ◽  
pp. 1092 ◽  
Author(s):  
Bilel Benjdira ◽  
Adel Ammar ◽  
Anis Koubaa ◽  
Kais Ouni
Keyword(s):  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Aerial Imagery ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Source Domain ◽  
Adversarial Networks ◽  
Semantic Label ◽  
Global Accuracy ◽  
The Cost

Despite the significant advances noted in semantic segmentation of aerial imagery, a considerable limitation is blocking its adoption in real cases. If we test a segmentation model on a new area that is not included in its initial training set, accuracy will decrease remarkably. This is caused by the domain shift between the new targeted domain and the source domain used to train the model. In this paper, we addressed this challenge and proposed a new algorithm that uses Generative Adversarial Networks (GAN) architecture to minimize the domain shift and increase the ability of the model to work on new targeted domains. The proposed GAN architecture contains two GAN networks. The first GAN network converts the chosen image from the target domain into a semantic label. The second GAN network converts this generated semantic label into an image that belongs to the source domain but conserves the semantic map of the target image. This resulting image will be used by the semantic segmentation model to generate a better semantic label of the first chosen image. Our algorithm is tested on the ISPRS semantic segmentation dataset and improved the global accuracy by a margin up to 24% when passing from Potsdam domain to Vaihingen domain. This margin can be increased by addition of other labeled data from the target domain. To minimize the cost of supervision in the translation process, we proposed a methodology to use these labeled data efficiently.

scholarly journals TriGAN: image-to-image translation for multi-source domain adaptation

2021 ◽  
Vol 32 (1) ◽  
Author(s):  
Subhankar Roy ◽  
Aliaksandr Siarohin ◽  
Enver Sangineto ◽  
Nicu Sebe ◽  
Elisa Ricci
Keyword(s):  
Domain Adaptation ◽  
Image Features ◽  
Generative Adversarial Networks ◽  
Source Image ◽  
Multiple Sources ◽  
Target Domain ◽  
Invariant Representation ◽  
Source Domain ◽  
Practical Applications ◽  
Adversarial Networks

AbstractMost domain adaptation methods consider the problem of transferring knowledge to the target domain from a single-source dataset. However, in practical applications, we typically have access to multiple sources. In this paper we propose the first approach for multi-source domain adaptation (MSDA) based on generative adversarial networks. Our method is inspired by the observation that the appearance of a given image depends on three factors: the domain, the style (characterized in terms of low-level features variations) and the content. For this reason, we propose to project the source image features onto a space where only the dependence from the content is kept, and then re-project this invariant representation onto the pixel space using the target domain and style. In this way, new labeled images can be generated which are used to train a final target classifier. We test our approach using common MSDA benchmarks, showing that it outperforms state-of-the-art methods.

scholarly journals Multi-Attribute Transfer via Disentangled Representation

2019 ◽  
Vol 33 ◽  
pp. 9195-9202 ◽  
Author(s):  
Jianfu Zhang ◽  
Yuanyuan Huang ◽  
Yaoyi Li ◽  
Weijie Zhao ◽  
Liqing Zhang
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Generative Adversarial Networks ◽  
Significant Progress ◽  
Target Domain ◽  
Adversarial Networks ◽  
Proposed Model ◽  
Image Translation ◽  
Realistic Images ◽  
Novel Model

Recent studies show significant progress in image-to-image translation task, especially facilitated by Generative Adversarial Networks. They can synthesize highly realistic images and alter the attribute labels for the images. However, these works employ attribute vectors to specify the target domain which diminishes image-level attribute diversity. In this paper, we propose a novel model formulating disentangled representations by projecting images to latent units, grouped feature channels of Convolutional Neural Network, to disassemble the information between different attributes. Thanks to disentangled representation, we can transfer attributes according to the attribute labels and moreover retain the diversity beyond the labels, namely, the styles inside each image. This is achieved by specifying some attributes and swapping the corresponding latent units to “swap” the attributes appearance, or applying channel-wise interpolation to blend different attributes. To verify the motivation of our proposed model, we train and evaluate our model on face dataset CelebA. Furthermore, the evaluation of another facial expression dataset RaFD demonstrates the generalizability of our proposed model.

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