7 citations · 12 across the 24 of their papers we have counts for
4 papers · 1 filter
Language-Guided Transformer for Federated Multi-Label Classification
I-Jieh Liu, Ci-Siang Lin, Fu-En Yang +1
Federated Learning (FL) is an emerging paradigm that enables multiple users to collaboratively train a robust model in a privacy-preserving manner without sharing their private dat…
Receler: Reliable Concept Erasing of Text-to-Image Diffusion Models via Lightweight Erasers
Chi-Pin Huang, Kai-Po Chang, Chung-Ting Tsai +3
Concept erasure in text-to-image diffusion models aims to disable pre-trained diffusion models from generating images related to a target concept. To perform reliable concept erasu…
Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation
Fu-En Yang, Chien-Yi Wang, Yu-Chiang Frank Wang
Federated learning (FL) emerges as a decentralized learning framework which trains models from multiple distributed clients without sharing their data to preserve privacy. Recently…
TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators
Yuan-Chia Cheng, Zu-Yun Shiau, Fu-En Yang +1
To understand how deep neural networks perform classification predictions, recent research attention has been focusing on developing techniques to offer desirable explanations. How…