43 citations · 160 across the 30 of their papers we have counts for
8 papers · 1 filter
Adversarial Purification through Representation Disentanglement
Tao Bai, Jun Zhao, Lanqing Guo +1
Deep learning models are vulnerable to adversarial examples and make incomprehensible mistakes, which puts a threat on their real-world deployment. Combined with the idea of advers…
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…
ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement
Rongkai Zhang, Lanqing Guo, Siyu Huang +1
Low-light image enhancement (LLIE) is a pervasive yet challenging problem, since: 1) low-light measurements may vary due to different imaging conditions in practice; 2) images can…
R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery
Rongkai Zhang, Jiang Zhu, Zhiyuan Zha +2
State-of-the-art image denoisers exploit various types of deep neural networks via deterministic training. Alternatively, very recent works utilize deep reinforcement learning for…
Reconciliation of Statistical and Spatial Sparsity For Robust Image and Image-Set Classification
Hao Cheng, Kim-Hui Yap, Bihan Wen
Recent image classification algorithms, by learning deep features from large-scale datasets, have achieved significantly better results comparing to the classic feature-based appro…
Systematic Analysis and Removal of Circular Artifacts for StyleGAN
Way Tan, Bihan Wen, Xulei Yang
StyleGAN is one of the state-of-the-art image generators which is well-known for synthesizing high-resolution and hyper-realistic face images. Though images generated by vanilla St…