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Generate To Adapt: Aligning Domains using Generative Adversarial Networks
[article]
2018
arXiv
pre-print
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(2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. ...
This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. ...
[31] and [1] use adversarial networks to map source images to target and perform adaptation in the transferred space. ...
arXiv:1704.01705v4
fatcat:qmaiwj277zahhbenkyyrs2lwoi
Generate to Adapt: Aligning Domains Using Generative Adversarial Networks
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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(2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. ...
This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. ...
Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. ...
doi:10.1109/cvpr.2018.00887
dblp:conf/cvpr/Sankaranarayanan18a
fatcat:y3ueuaswxjbrdhbxgsz2kacbqu
Synthetic to Real Adaptation with Generative Correlation Alignment Networks
[article]
2017
arXiv
pre-print
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In this work, we propose a Deep Generative Correlation Alignment Network (DGCAN) to synthesize images using a novel domain adaption algorithm. ...
Recent work has shown the great potential of deep convolutional neural networks to generate realistic images, but has not utilized generative models to address synthetic-to-real domain adaptation. ...
Deep domain adaptation methods address the domain shift by adding one or multiple adaptation layers and losses [41, 40, 20, 38] , or use an adversarial network to match the source distribution to target ...
arXiv:1701.05524v3
fatcat:mvzexxnr4jaarpwdzbudquj3im
Generative Adversarial Networks for Video-to-Video Domain Adaptation
[article]
2020
arXiv
pre-print
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In this work, we propose a novel generative adversarial network (GAN), namely VideoGAN, to transfer the video-based data across different domains. ...
., color and illumination, which make the models trained on one domain usually fail to generalize well to another. Domain adaptation is one of the potential solutions to address the problem. ...
In this paper, we propose a novel generative adversarial network, namely VideoGAN, for domain adaptation of multicentre endoscopic videos. ...
arXiv:2004.08058v1
fatcat:72bmzh6zujadnpltdksawg27i4
Generative Adversarial Networks for Video-to-Video Domain Adaptation
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this work, we propose a novel generative adversarial network (GAN), namely VideoGAN, to transfer the video-based data across different domains. ...
., color and illumination, which make the models trained on one domain usually fail to generalize well to another. Domain adaptation is one of the potential solutions to address the problem. ...
In this paper, we propose a novel generative adversarial network, namely VideoGAN, for domain adaptation of multicentre endoscopic videos. ...
doi:10.1609/aaai.v34i04.5750
fatcat:w6ywcjpzhjdfvjeny6jlspoacq
Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation
[article]
2020
arXiv
pre-print
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Instead of assuming the domain gap has been eliminated, we minimize the discrepancy between the generated data and real data while learning a degradation adaptive SR network (i.e., learning to zoom in) ...
To do so, we firstly train a degradation generation network to generate realistic LR images and, more importantly, to capture their distribution (i.e., learning to zoom out). ...
We propose an adaptive loss to align the network responses of I L gen and I L real , which is designed as a adversarial loss between the features of I L gen and I L real : L ada ( R, D ada ) = E I L gen ...
arXiv:2001.02381v1
fatcat:bxy4me2mirhkpa4azrczliaflq
Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2108.09178v1 fatcat:zbcqia27obhh3jprctgx2em4zu
Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this issue. ...
However, pre-trained networks often fail to generalize to new test domains that are not distributed identically due to tissue stainings, types, and textures variations. ...
The authors would like to thank Dr. Felix Müller, M. Med. ...
arXiv:2108.09178v2
fatcat:nhoqz2gx4rbwlbcjzcfcpfevga
A General Approach to Domain Adaptation with Applications in Astronomy
[article]
2018
arXiv
pre-print
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In this paper we propose a new general approach to domain adaptation that does not rely on the proximity of source and target distributions. ...
The problem is generally known as transfer learning in machine learning, where domain adaptation is a popular scenario. ...
Domain-Adversarial Training of Neural Networks (DATNN) [57] . ...
arXiv:1812.08839v1
fatcat:vg4z7svc7vgihnwytjjy6my2im
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
[article]
2020
arXiv
pre-print
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Experimental results show the superiority of the proposed method compared to the existing state-of-the-art models with a fixed network architecture and hyper-parameters. ...
Moreover, our new AdaLIN (Adaptive Layer-Instance Normalization) function helps our attention-guided model to flexibly control the amount of change in shape and texture by learned parameters depending ...
UNSUPERVISED GENERATIVE ATTENTIONAL NETWORKS WITH ADAPTIVE LAYER-INSTANCE NORMALIZATION Our goal is to train a function G s→t that maps images from a source domain X s to a target domain X t using only ...
arXiv:1907.10830v4
fatcat:u4bey3rm4zh27cyl6zmljgrfwu
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control
[article]
2020
arXiv
pre-print
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parameters to use. ...
We propose Adaptive Curriculum Generation from Demonstrations (ACGD) for reinforcement learning in the presence of sparse rewards. ...
A more evolved approach generates a curriculum of goals using a Generative Adversarial Network (GAN) but cannot handle visual goals [11] . ...
arXiv:1910.07972v3
fatcat:dexm2xv3jzfn7ihrqkyixkeb2u
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation
[article]
2021
arXiv
pre-print
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Specifically, we propose Style-Adaptive Layer Normalization (SALN) which aligns gain and bias of the text input according to the style extracted from a reference speech audio. ...
With rapid progress in neural text-to-speech (TTS) models, personalized speech generation is now in high demand for many applications. ...
We sincerely thank the anonymous reviewers for their constructive comments which helped us significantly improve our paper during the rebuttal period. ...
arXiv:2106.03153v3
fatcat:fqlq3xyt5fbalbjvli4eq7gfk4
Generalizing to Unseen Domains: A Survey on Domain Generalization
[article]
2022
arXiv
pre-print
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Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain. ...
We then thoroughly review the theories related to domain generalization and carefully analyze the theory behind generalization. ...
[92] proposed Domain-adversarial neural network (DANN) for domain adaptation, which adversarially trains the generator and discriminator. ...
arXiv:2103.03097v7
fatcat:ry4ggjl63bhlzdhg3gojvyk2v4
Self-adaptive Re-weighted Adversarial Domain Adaptation
[article]
2020
arXiv
pre-print
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To address this problem, we present a self-adaptive re-weighted adversarial domain adaptation approach, which tries to enhance domain alignment from the perspective of conditional distribution. ...
Existing adversarial domain adaptation methods mainly consider the marginal distribution and these methods may lead to either under transfer or negative transfer. ...
Our method aims to construct a target generalized network. The re-weighted adversarial domain adaptation forces a close global domain-level alignment. ...
arXiv:2006.00223v2
fatcat:nsreyyy32jakljejrerlatfyya
Generic Indic Text-to-speech Synthesisers with Rapid Adaptation in an End-to-end Framework
[article]
2020
arXiv
pre-print
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These systems are then adapted to a new language in the same family using small amounts of adaptation data. ...
The proposed work exploits this property to build a generic TTS system using multiple languages from the same family in an end-to-end framework. ...
The authors would like to thank the Ministry of Electronics and Information Technology (MeitY), Government of India, for funding the project, "Text to Speech Generation with chosen accent and noise profile ...
arXiv:2006.06971v1
fatcat:uhxwuf5vw5c37lm3iogbgteljq
Learning to Generate Novel Domains for Domain Generalization
[article]
2021
arXiv
pre-print
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To train the generator, we model the distribution divergence between source and synthesized pseudo-novel domains using optimal transport, and maximize the divergence. ...
This paper focuses on domain generalization (DG), the task of learning from multiple source domains a model that generalizes well to unseen domains. ...
generator network. ...
arXiv:2007.03304v3
fatcat:esfi3fkksngxbfjbbzwu6lqkqi
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