168 citations · 259 across the 18 of their papers we have counts for
14 papers · 1 filter
Constrained Maximum Cross-Domain Likelihood for Domain Generalization
Jianxin Lin, Yongqiang Tang, Junping Wang +1
As a recent noticeable topic, domain generalization aims to learn a generalizable model on multiple source domains, which is expected to perform well on unseen test domains. Great…
Mitigating Both Covariate and Conditional Shift for Domain Generalization
Jianxin Lin, Yongqiang Tang, Junping Wang +1
Domain generalization (DG) aims to learn a model on several source domains, hoping that the model can generalize well to unseen target domains. The distribution shift between domai…
Image-to-Image Translation: Methods and Applications
Yingxue Pang, Jianxin Lin, Tao Qin +1
Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations. I2I has drawn increasing attention an…
Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration
Xin Li, Xin Jin, Jianxin Lin +5
Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions. Existing HD-IR approaches usually ignore the inher…
TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images
Jianxin Lin, Yingxue Pang, Yingce Xia +2
An unsupervised image-to-image translation (UI2I) task deals with learning a mapping between two domains without paired images. While existing UI2I methods usually require numerous…
ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image Translation
Jianxin Lin, Yingce Xia, Sen Liu +2
Image-to-image translation models have shown remarkable ability on transferring images among different domains. Most of existing work follows the setting that the source domain and…
Learning to Transfer: Unsupervised Meta Domain Translation
Jianxin Lin, Yijun Wang, Tianyu He +1
Unsupervised domain translation has recently achieved impressive performance with Generative Adversarial Network (GAN) and sufficient (unpaired) training data. However, existing do…
Image-to-Image Translation with Multi-Path Consistency Regularization
Jianxin Lin, Yingce Xia, Yijun Wang +2
Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from source domain $\mathca…
Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation
Jianxin Lin, Zhibo Chen, Yingce Xia +3
Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs). However, existing approaches are mostly designed in an unsupervised mann…
Sequential Gating Ensemble Network for Noise Robust Multi-Scale Face Restoration
Zhibo Chen, Jianxin Lin, Tiankuang Zhou +1
Face restoration from low resolution and noise is important for applications of face analysis recognition. However, most existing face restoration models omit the multiple scale is…
Unsupervised Single Image Deraining with Self-supervised Constraints
Xin Jin, Zhibo Chen, Jianxin Lin +2
Most existing single image deraining methods require learning supervised models from a large set of paired synthetic training data, which limits their generality, scalability and p…
DeepIR: A Deep Semantics Driven Framework for Image Retargeting
Jianxin Lin, Tiankuang Zhou, Zhibo Chen
We present \emph{Deep Image Retargeting} (\emph{DeepIR}), a coarse-to-fine framework for content-aware image retargeting. Our framework first constructs the semantic structure of i…
Multi-Scale Face Restoration with Sequential Gating Ensemble Network
Jianxin Lin, Tiankuang Zhou, Zhibo Chen
Restoring face images from distortions is important in face recognition applications and is challenged by multiple scale issues, which is still not well-solved in research area. In…
Conditional Image-to-Image Translation
Jianxin Lin, Yingce Xia, Tao Qin +2
Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs) and dual learning. However, existing models lack the ability to control t…