Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning

TL;DR

This paper introduces ALDC, a novel method for Semi-FSCIL that effectively distinguishes and calibrates distributions of base and novel classes using unlabeled data, improving performance in realistic scenarios.

Contribution

The paper redefines Semi-FSCIL as GSemi-FSCIL to include all seen classes in unlabeled data and proposes ALDC, a dynamic distribution calibration strategy that enhances learning from unlabeled samples.

Findings

ALDC outperforms existing methods on three benchmark datasets.

The approach effectively distinguishes between base and novel class samples.

Results demonstrate state-of-the-art performance in semi-supervised few-shot class-incremental learning.

Abstract

Few-Shot Class-Incremental Learning (FSCIL) focuses on models learning new concepts from limited data while retaining knowledge of previous classes. Recently, many studies have started to leverage unlabeled samples to assist models in learning from few-shot samples, giving rise to the field of Semi-supervised Few-shot Class-Incremental Learning (Semi-FSCIL). However, these studies often assume that the source of unlabeled data is only confined to novel classes of the current session, which presents a narrow perspective and cannot align well with practical scenarios. To better reflect real-world scenarios, we redefine Semi-FSCIL as Generalized Semi-FSCIL (GSemi-FSCIL) by incorporating both base and all the ever-seen novel classes in the unlabeled set. This change in the composition of unlabeled samples poses a new challenge for existing methods, as they struggle to distinguish between unlabeled samples from base and novel classes. To address this issue, we propose an Ambiguity-guided Learnable Distribution Calibration (ALDC) strategy. ALDC dynamically uses abundant base samples to correct biased feature distributions for few-shot novel classes. Experiments on three benchmark datasets show that our method outperforms existing works, setting new state-of-the-art results.