提出新方法解决少样本增量学习中混淆数据来源的问题。
Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning
- 用不确定度引导动态校准特征分布，区分基础类和新类
- 在三个基准数据集上达到新最好效果，性能显著提升
- 适合需要持续学习新类别且数据有限的实用场景
少样本增量学习（FSCIL）旨在模型仅用少量数据学习新概念的同时保留旧知识。近期研究开始利用未标注样本辅助少样本学习，形成半监督少样本增量学习（Semi-FSCIL）。然而，现有方法通常假设未标注数据仅来自当前会话的新类别，视角局限，难以贴近真实场景。为此，我们重新定义为广义半监督少样本增量学习（GSemi-FSCIL），将基础类和所有已见过的新类均纳入未标注集。这一变化使现有方法难以区分未标注样本来源，带来新挑战。为此，我们提出不确定度引导的可学习分布校准（ALDC）策略：通过大量基础样本动态校正少样本新类的特征分布偏差。在三个基准数据集上的实验表明，该方法优于现有工作，刷新了当前最佳性能。
原文摘要 · Abstract (English)
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.
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