Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapters

TL;DR

This paper introduces a new framework called CMoA for generalized few-shot continual learning, addressing both class and domain incremental challenges and evaluating domain generalization, with a focus on improving adaptability and reducing forgetting.

Contribution

The paper proposes a novel rehearsal-free ViT-based framework, CMoA, that handles class and domain incremental learning simultaneously and assesses domain generalization capabilities.

Findings

Common methods show poor generalization on unseen domains.

CMoA improves domain-invariant representations and reduces catastrophic forgetting.

Benchmark results demonstrate the effectiveness of CMoA in generalized FSCL scenarios.

Abstract

The goal of Few-Shot Continual Learning (FSCL) is to incrementally learn novel tasks with limited labeled samples and preserve previous capabilities simultaneously, while current FSCL methods are all for the class-incremental purpose. Moreover, the evaluation of FSCL solutions is only the cumulative performance of all encountered tasks, but there is no work on exploring the domain generalization ability. Domain generalization is a challenging yet practical task that aims to generalize beyond training domains. In this paper, we set up a Generalized FSCL (GFSCL) protocol involving both class- and domain-incremental situations together with the domain generalization assessment. Firstly, two benchmark datasets and protocols are newly arranged, and detailed baselines are provided for this unexplored configuration. We find that common continual learning methods have poor generalization ability on unseen domains and cannot better cope with the catastrophic forgetting issue in cross-incremental tasks. In this way, we further propose a rehearsal-free framework based on Vision Transformer (ViT) named Contrastive Mixture of Adapters (CMoA). Due to different optimization targets of class increment and domain increment, the CMoA contains two parts: (1) For the class-incremental issue, the Mixture of Adapters (MoA) module is incorporated into ViT, then cosine similarity regularization and the dynamic weighting are designed to make each adapter learn specific knowledge and concentrate on particular classes. (2) For the domain-related issues and domain-invariant representation learning, we alleviate the inner-class variation by prototype-calibrated contrastive learning. The codes and protocols are available at https://github.com/yawencui/CMoA.