16 citations · 33 across the 17 of their papers we have counts for
6 papers · 1 filter
Class Gradient Projection For Continual Learning
Cheng Chen, Ji Zhang, Jingkuan Song +1
Catastrophic forgetting is one of the most critical challenges in Continual Learning (CL). Recent approaches tackle this problem by projecting the gradient update orthogonal to the…
From Channel Bias to Feature Redundancy: Uncovering the "Less is More" Principle in Few-Shot Learning
Ji Zhang, Xu Luo, Lianli Gao +3
Deep neural networks often fail to adapt representations to novel tasks under distribution shifts, especially when only a few examples are available. This paper identifies a core o…
DePT: Decoupled Prompt Tuning
Ji Zhang, Shihan Wu, Lianli Gao +2
This work breaks through the Base-New Tradeoff (BNT)dilemma in prompt tuning, i.e., the better the tuned model generalizes to the base (or target) task, the worse it generalizes to…
From Global to Local: Multi-scale Out-of-distribution Detection
Ji Zhang, Lianli Gao, Bingguang Hao +3
Out-of-distribution (OOD) detection aims to detect "unknown" data whose labels have not been seen during the in-distribution (ID) training process. Recent progress in representatio…
DETA: Denoised Task Adaptation for Few-Shot Learning
Ji Zhang, Lianli Gao, Xu Luo +2
Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge of the test task, rely only on few-labeled suppo…
A Closer Look at Few-shot Classification Again
Xu Luo, Hao Wu, Ji Zhang +3
Few-shot classification consists of a training phase where a model is learned on a relatively large dataset and an adaptation phase where the learned model is adapted to previously…