43 citations · 131 across the 23 of their papers we have counts for
7 papers · 1 filter
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix
Wenbin Li, Meihao Kong, Xuesong Yang +4
In recent years, a variety of contrastive learning based unsupervised visual representation learning methods have been designed and achieved great success in many visual tasks. Gen…
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient
Wubing Chen, Wenbin Li, Xiao Liu +2
Cooperative multi-agent policy gradient (MAPG) algorithms have recently attracted wide attention and are regarded as a general scheme for the multi-agent system. Credit assignment…
Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery
Wenbin Li, Zhichen Fan, Jing Huo +1
Novel class discovery (NCD) aims at learning a model that transfers the common knowledge from a class-disjoint labelled dataset to another unlabelled dataset and discovers new clas…
Uniformly convex neural networks and non-stationary iterated network Tikhonov (iNETT) method
Davide Bianchi, Guanghao Lai, Wenbin Li
We propose a non-stationary iterated network Tikhonov (iNETT) method for the solution of ill-posed inverse problems. The iNETT employs deep neural networks to build a data-driven r…
Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation
Min Zhang, Siteng Huang, Wenbin Li +1
In this paper, we mainly focus on the problem of how to learn additional feature representations for few-shot image classification through pretext tasks (e.g., rotation or color pe…
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…