120 citations · 175 across the 9 of their papers we have counts for
5 papers · 1 filter
Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions
Chenyu Yi, Siyuan Yang, Haoliang Li +2
The state-of-the-art deep neural networks are vulnerable to common corruptions (e.g., input data degradations, distortions, and disturbances caused by weather changes, system error…
Disentangled Feature Representation for Few-shot Image Classification
Hao Cheng, Yufei Wang, Haoliang Li +2
Learning the generalizable feature representation is critical for few-shot image classification. While recent works exploited task-specific feature embedding using meta-tasks for f…
Low-Light Image Enhancement with Normalizing Flow
Yufei Wang, Renjie Wan, Wenhan Yang +3
To enhance low-light images to normally-exposed ones is highly ill-posed, namely that the mapping relationship between them is one-to-many. Previous works based on the pixel-wise r…
Variational Disentanglement for Domain Generalization
Yufei Wang, Haoliang Li, Hao Cheng +3
Domain generalization aims to learn an invariant model that can generalize well to the unseen target domain. In this paper, we propose to tackle the problem of domain generalizatio…
Embracing the Dark Knowledge: Domain Generalization Using Regularized Knowledge Distillation
Yufei Wang, Haoliang Li, Lap-pui Chau +1
Though convolutional neural networks are widely used in different tasks, lack of generalization capability in the absence of sufficient and representative data is one of the challe…