Conventional Few-shot classification (FSC) aims to recognize samples from novel classes given limited labeled data. Recently, domain generalization FSC (DG-FSC) has been proposed with the goal to recognize novel class samples from unseen domains. DG-FSC poses considerable challenges to many models due to the domain shift between base classes (used in training) and novel classes (encountered in evaluation). In this work, we make two novel contributions to tackle DG-FSC. Our first contribution is to propose Born-Again Network (BAN) episodic training and comprehensively investigate its effectiveness for DG-FSC. As a specific form of knowledge distillation, BAN has been shown to achieve improved generalization in conventional supervised classification with a closed-set setup. This improved generalization motivates us to study BAN for DG-FSC, and we show that BAN is promising to address the domain shift encountered in DG-FSC. Building on the encouraging finding, our second (major) contribution is to propose few-shot BAN, FS-BAN, a novel BAN approach for DG-FSC. Our proposed FS-BAN includes novel multi-task learning objectives: Mutual Regularization, Mismatched Teacher and Meta-Control Temperature, each of these is specifically designed to overcome central and unique challenges in DG-FSC, namely overfitting and domain discrepancy. We analyze different design choices of these techniques. We conduct comprehensive quantitative and qualitative analysis and evaluation using six datasets and three baseline models. The results suggest that our proposed FS-BAN consistently improves the generalization performance of baseline models and achieves state-of-the-art accuracy for DG-FSC.
翻译:常规的少发分类(FSC)旨在识别来自新类的样本,因为标签数据有限。最近,提出了域性一般化FSC(DG-FSC)建议,目的是承认来自隐蔽域的新类样本。DG-FSC(DG-FSC)由于基础班(用于培训)和新类班(用于评价)之间的域变,对许多模型提出了相当大的挑战。在这项工作中,我们作出了两项新的贡献,以解决DG-FSC。我们的第一个贡献是提出Born-Again网络(BAN)的累积性培训,并全面调查DG-FSC(D-FSC)的功效。作为知识蒸馏的一种具体形式,已经表明在常规监督的分类中,通过封闭式设置系统化的架构,将改进常规监督性分类的普及化。DG-FSC(用于培训)和新课程(在评价中)的域内,我们提议的SFSB-B-B(FSC)质量选择的新颖性选择(FS-BAN-C)的基线和新颖性分析,具体地(我们提出的三套FS-B-B-BAS-BAS-S-S-S-S-SD-C)核心分析,即:我们设计的常规性定义和核心性定义和核心性分析,即常规性分析,即常规性分析,即:我们设计的常规性分析,即常规性分析,即常规性分析,即常规性分析,这些核心性分析,即常规性分析,这些核心性分析,这些核心性分析,即常规性分析,即常规性分析,这些核心性分析,即常规性分析,即常规性分析,即常规性能和核心性分析,即常规性分析,这些核心性分析,这些核心性分析,即常规性分析,这些核心性分析,即常规性分析,即常规性分析,即常规性分析,即常规性分析,即常规性分析,这些核心性分析。