43 citations · 150 across the 24 of their papers we have counts for
5 papers · 1 filter
Improving Multi-Modal Learning with Uni-Modal Teachers
Chenzhuang Du, Tingle Li, Yichen Liu +4
Learning multi-modal representations is an essential step towards real-world robotic applications, and various multi-modal fusion models have been developed for this purpose. Howev…
Intrinsically Motivated Self-supervised Learning in Reinforcement Learning
Yue Zhao, Chenzhuang Du, Hang Zhao +1
In vision-based reinforcement learning (RL) tasks, it is prevalent to assign auxiliary tasks with a surrogate self-supervised loss so as to obtain more semantic representations and…
What Makes Multi-modal Learning Better than Single (Provably)
Yu Huang, Chenzhuang Du, Zihui Xue +3
The world provides us with data of multiple modalities. Intuitively, models fusing data from different modalities outperform their uni-modal counterparts, since more information is…
On Feature Decorrelation in Self-Supervised Learning
Tianyu Hua, Wenxiao Wang, Zihui Xue +3
In self-supervised representation learning, a common idea behind most of the state-of-the-art approaches is to enforce the robustness of the representations to predefined augmentat…
SEMI: Self-supervised Exploration via Multisensory Incongruity
Jianren Wang, Ziwen Zhuang, Hang Zhao
Efficient exploration is a long-standing problem in reinforcement learning since extrinsic rewards are usually sparse or missing. A popular solution to this issue is to feed an age…