Class-Incremental Learning With Strong Pre-Trained Models
Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li, Avinash Ravichandran, Nuno Vasconcelos, Rahul Bhotika, Stefano Soatto
Abstract
Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world setting of CIL that starts with a strong model pre-trained on a large number of base classes. We hypothesize that a strong base model can provide a good representation for novel classes and incremental learning can be done with small adaptations. We propose a 2-stage training scheme, i) feature augmentation - cloning part of the backbone and fine-tuning it on the novel data, and ii) fusion - combining the base and novel classifiers into a unified classifier. Experiments show that the proposed method significantly outperforms state-of-the-art CIL methods on the large-scale ImageNet dataset (e.g. +10% overall accuracy than the best). We also propose and analyze understudied practical CIL scenarios, such as base-novel overlap with distribution shift. Our proposed method is robust and generalizes to all analyzed CIL settings.
BibTeX
@inproceedings{cvpr2022_classincremental,
title = {Class-Incremental Learning With Strong Pre-Trained Models},
author = {Tz-Ying Wu and Gurumurthy Swaminathan and Zhizhong Li and Avinash Ravichandran and Nuno Vasconcelos and Rahul Bhotika and Stefano Soatto},
booktitle = {CVPR 2022},
year = {2022}
}