Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration

1 code implementation • NeurIPS 2023 • Qi-Wei Wang, Da-Wei Zhou, Yi-Kai Zhang, De-Chuan Zhan, Han-Jia Ye

In this Few-Shot Class-Incremental Learning (FSCIL) scenario, existing methods either introduce extra learnable components or rely on a frozen feature extractor to mitigate catastrophic forgetting and overfitting problems.

Few-Shot Class-Incremental Learning Few-Shot Learning +3

ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse

1 code implementation • 17 Aug 2023 • Yi-Kai Zhang, Lu Ren, Chao Yi, Qi-Wei Wang, De-Chuan Zhan, Han-Jia Ye

The rapid expansion of foundation pre-trained models and their fine-tuned counterparts has significantly contributed to the advancement of machine learning.

Learning Debiased Representations via Conditional Attribute Interpolation

1 code implementation • CVPR 2023 • Yi-Kai Zhang, Qi-Wei Wang, De-Chuan Zhan, Han-Jia Ye

When a dataset is biased, i. e., most samples have attributes spuriously correlated with the target label, a Deep Neural Network (DNN) is prone to make predictions by the "unintended" attribute, especially if it is easier to learn.

Attribute Metric Learning