解决跨域少样本增量音频分类难题，提升模型泛化能力。
Cross Domain Few-Shot Class-Incremental Audio Classification Via Adversarial Contrastive Learning
- 采用对抗性对比学习，让模型适应未见领域的音频特征。
- 在六组跨域数据集上平均准确率超越现有方法。
- 适合需要持续学习新类别且数据分布变化的音频场景。
当前少样本增量音频分类（FCAC）方法假设基础类与增量类样本来自相同领域（服从相同分布），但实际中两者常存在领域差异。本文研究跨域少样本增量音频分类问题，提出一种对抗性对比学习策略，使模型能够有效识别来自未见领域的不同类别音频。模型由编码器和分类器组成：编码器在基础阶段训练后冻结，分类器在所有阶段持续更新。在六对跨域数据集上的实验表明，该方法在平均准确率上优于现有先进方法。代码已公开于 https://github.com/YongjieSi/ACL。
原文摘要 · Abstract (English)
Current Few-shot Class-incremental Audio Classification (FCAC) methods assume that samples of base and incremental classes are in the same domain (following the same distribution). However, there is generally a domain shift between the above two types of samples. In this paper, we explore the problem of Cross Domain FCAC where samples of base and incremental classes have domain shift. We propose a strategy of adversarial contrastive training which enables the model to effectively classify samples of different classes from unseen domains. The model consists of an encoder and a classifier. The encoder is trained in base session but frozen in incremental sessions, whereas the classifier is trained in all sessions. Experiments are done on six pairs of cross-domain datasets. Results show that our method exceeds state-of-the-art methods in average accuracy. The code is at https://github.com/YongjieSi/ACL.
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