Related papers: Can Synthetic Images Conquer Forgetting? Beyond Unexplored Doubts in Few-Shot Class-Incremental Learning

Can Synthetic Images Conquer Forgetting? Beyond Unexplored Doubts in Few-Shot Class-Incremental Learning

URL: http://arxiv.org/abs/2507.13739v1
Date: Fri, 18 Jul 2025 08:38:07 GMT
Title: Can Synthetic Images Conquer Forgetting? Beyond Unexplored Doubts in Few-Shot Class-Incremental Learning
Authors: Junsu Kim, Yunhoe Ku, Seungryul Baek,
Abstract summary: Few-shot class-incremental learning (FSCIL) is challenging due to extremely limited training data.<n>We propose Diffusion-FSCIL, a novel approach that employs a text-to-image diffusion model as a frozen backbone.
Score: 9.73590544210575
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Few-shot class-incremental learning (FSCIL) is challenging due to extremely limited training data; while aiming to reduce catastrophic forgetting and learn new information. We propose Diffusion-FSCIL, a novel approach that employs a text-to-image diffusion model as a frozen backbone. Our conjecture is that FSCIL can be tackled using a large generative model's capabilities benefiting from 1) generation ability via large-scale pre-training; 2) multi-scale representation; 3) representational flexibility through the text encoder. To maximize the representation capability, we propose to extract multiple complementary diffusion features to play roles as latent replay with slight support from feature distillation for preventing generative biases. Our framework realizes efficiency through 1) using a frozen backbone; 2) minimal trainable components; 3) batch processing of multiple feature extractions. Extensive experiments on CUB-200, \emph{mini}ImageNet, and CIFAR-100 show that Diffusion-FSCIL surpasses state-of-the-art methods, preserving performance on previously learned classes and adapting effectively to new ones.

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