通过特征增强提升旧类分离，解决少样本增量学习中旧类性能下降问题。
Strategic Base Representation Learning via Feature Augmentations for Few-Shot Class Incremental Learning
- 用特征增强和代理标签扩展特征空间，增强旧类分离
- 在三个基准数据集上显著优于现有方法，表现达到新纪录
- 适合关注少样本增量学习中知识保留与类间分离的研究者
少样本类增量学习要求模型在仅用少量样本的情况下学习新类别，同时保留对已学类别的知识。现有方法通常在引入新类别时冻结旧类参数，但常导致旧类间分离度不足，造成旧类与新类重叠，进而损害旧类性能。为此，我们提出一种基于特征增强的对比学习框架，通过增强特征向量并为其分配代理标签，扩大特征空间，实现新类在扩展空间中的无缝融合。同时，采用自监督对比损失强化旧类间的分离性。我们在CIFAR100、miniImageNet和CUB200三个FSCIL基准数据集上验证了该框架，实验结果表明，所提方法显著优于现有方法，达到当前最优性能。
原文摘要 · Abstract (English)
Few-shot class incremental learning implies the model to learn new classes while retaining knowledge of previously learned classes with a small number of training instances. Existing frameworks typically freeze the parameters of the previously learned classes during the incorporation of new classes. However, this approach often results in suboptimal class separation of previously learned classes, leading to overlap between old and new classes. Consequently, the performance of old classes degrades on new classes. To address these challenges, we propose a novel feature augmentation driven contrastive learning framework designed to enhance the separation of previously learned classes to accommodate new classes. Our approach involves augmenting feature vectors and assigning proxy labels to these vectors. This strategy expands the feature space, ensuring seamless integration of new classes within the expanded space. Additionally, we employ a self-supervised contrastive loss to improve the separation between previous classes. We validate our framework through experiments on three FSCIL benchmark datasets: CIFAR100, miniImageNet, and CUB200. The results demonstrate that our Feature Augmentation driven Contrastive Learning framework significantly outperforms other approaches, achieving state-of-the-art performance.
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