4 citations · 6 across the 8 of their papers we have counts for
8 papers
Quantum-Gated Task-interaction Knowledge Distillation for Pre-trained Model-based Class-Incremental Learning
Linjie Li, Huiyu Xiao, Jiarui Cao +2
Class-incremental learning (CIL) aims to continuously accumulate knowledge from a stream of tasks and construct a unified classifier over all seen classes. Although pretrained mode…
LDEPrompt: Layer-importance guided Dual Expandable Prompt Pool for Pre-trained Model-based Class-Incremental Learning
Linjie Li, Zhenyu Wu, Huiyu Xiao +1
Prompt-based class-incremental learning methods typically construct a prompt pool consisting of multiple trainable key-prompts and perform instance-level matching to select the mos…
MoTE: Mixture of Task-specific Experts for Pre-Trained ModelBased Class-incremental Learning
Linjie Li, Zhenyu Wu, Yang Ji
Class-incremental learning (CIL) requires deep learning models to continuously acquire new knowledge from streaming data while preserving previously learned information. Recently,…
Contrastive Conditional Alignment based on Label Shift Calibration for Imbalanced Domain Adaptation
Xiaona Sun, Zhenyu Wu, Zhiqiang Zhan +1
Many existing unsupervised domain adaptation (UDA) methods primarily focus on covariate shift, limiting their effectiveness in imbalanced domain adaptation (IDA) where both covaria…
Decoupled Federated Learning on Long-Tailed and Non-IID data with Feature Statistics
Zhuoxin Chen, Zhenyu Wu, Yang Ji
Federated learning is designed to enhance data security and privacy, but faces challenges when dealing with heterogeneous data in long-tailed and non-IID distributions. This paper…
centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation
Xiaona Sun, Zhenyu Wu, Yichen Liu +3
Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that…