4 papers
Physical Self-Supervised Learning: IMU Sensing without Manual Labels
Yuyang Leng, Renyuan Liu, Shaohan Hu +4
Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogene…
ECA: Efficient Continual Alignment for Open-Ended Image-to-Text Generation
Jiangtao Kong, Peijun Zhao, Chun-Fu Chen +4
Incremental Learning (IL) for Open-ended Image-to-Text Generation (OpenITG) enables models to continuously generate accurate, contextually relevant text for new images while preser…
DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training
Renyuan Liu, Yuyang Leng, Kaiyan Liu +6
Recent advancements in on-device training for deep neural networks have underscored the critical need for efficient activation compression to overcome the memory constraints of mob…
PASS: Private Attributes Protection with Stochastic Data Substitution
Yizhuo Chen, Chun-Fu, Chen +3
The growing Machine Learning (ML) services require extensive collections of user data, which may inadvertently include people's private information irrelevant to the services. Vari…