Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning

TL;DR

This paper introduces UaD-CE, a semi-supervised learning framework for Few-Shot Class-Incremental Learning that effectively utilizes unlabeled data through uncertainty-aware distillation and class-balanced self-training.

Contribution

The paper proposes a novel semi-supervised FSCIL framework with modules for uncertainty-guided distillation and class-equilibrium, addressing the adaptability of unlabeled data in incremental learning.

Findings

Boosts adaptability of unlabeled data in FSCIL tasks.

Improves performance on benchmark datasets.

Effectively balances class distribution during pseudo-labeling.

Abstract

Given a model well-trained with a large-scale base dataset, Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding overfitting, without catastrophically forgetting all encountered classes previously. Currently, semi-supervised learning technique that harnesses freely-available unlabeled data to compensate for limited labeled data can boost the performance in numerous vision tasks, which heuristically can be applied to tackle issues in FSCIL, i.e., the Semi-supervised FSCIL (Semi-FSCIL). So far, very limited work focuses on the Semi-FSCIL task, leaving the adaptability issue of semi-supervised learning to the FSCIL task unresolved. In this paper, we focus on this adaptability issue and present a simple yet efficient Semi-FSCIL framework named Uncertainty-aware Distillation with Class-Equilibrium (UaD-CE), encompassing two modules UaD and CE. Specifically, when incorporating unlabeled data into each incremental session, we introduce the CE module that employs a class-balanced self-training to avoid the gradual dominance of easy-to-classified classes on pseudo-label generation. To distill reliable knowledge from the reference model, we further implement the UaD module that combines uncertainty-guided knowledge refinement with adaptive distillation. Comprehensive experiments on three benchmark datasets demonstrate that our method can boost the adaptability of unlabeled data with the semi-supervised learning technique in FSCIL tasks.