43 citations · 47 across the 5 of their papers we have counts for
8 papers
Playing Lottery Tickets in Style Transfer Models
Meihao Kong, Jing Huo, Wenbin Li +3
Style transfer has achieved great success and attracted a wide range of attention from both academic and industrial communities due to its flexible application scenarios. However,…
Keeping Minimal Experience to Achieve Efficient Interpretable Policy Distillation
Xiao Liu, Shuyang Liu, Wenbin Li +2
Although deep reinforcement learning has become a universal solution for complex control tasks, its real-world applicability is still limited because lacking security guarantees fo…
Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification
Wei Zhu, Haofu Liao, Wenbin Li +2
Skin disease classification from images is crucial to dermatological diagnosis. However, identifying skin lesions involves a variety of aspects in terms of size, color, shape, and…
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
Tiexin Qin, Wenbin Li, Yinghuan Shi +1
Few-shot learning aims to learn a new concept when only a few training examples are available, which has been extensively explored in recent years. However, most of the current wor…
Asymmetric Distribution Measure for Few-shot Learning
Wenbin Li, Lei Wang, Jing Huo +3
The core idea of metric-based few-shot image classification is to directly measure the relations between query images and support classes to learn transferable feature embeddings.…
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
Da Chen, Yongliang Yang, Zunlei Feng +6
Few-shot image classification requires the classifier to robustly cope with unseen classes even if there are only a few samples for each class. Recent advances benefit from the met…