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 findings, 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 over 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. Project Page: https://yunqing-me.github.io/Born-Again-FS/.

翻译：摘要：传统小样本分类（FSC）旨在从有限标注数据中识别新类别的样本。近年来，域泛化小样本分类（DG-FSC）被提出，其目标是从未见过的域中识别新类别样本。由于基类（训练阶段使用）与新类（测试阶段遇到）之间存在域偏移，DG-FSC对许多模型提出了巨大挑战。本文为应对DG-FSC做出两项新贡献。第一项贡献是提出重生网络（BAN）情景训练，并全面探究其在DG-FSC中的有效性。作为知识蒸馏的一种特殊形式，BAN已被证明能在封闭集设定的传统监督分类中提升泛化能力。这种泛化能力的提升促使我们研究BAN在DG-FSC中的应用，并表明BAN有望解决DG-FSC中的域偏移问题。基于这一令人鼓舞的发现，我们的第二项（主要）贡献是提出小样本BAN（FS-BAN），一种面向DG-FSC的新型BAN方法。所提出的FS-BAN包含新颖的多任务学习目标：互正则化、不匹配教师与元控制温度，每项设计均针对DG-FSC的核心独特挑战（即过拟合与域差异）而专门提出。我们分析了这些技术的不同设计选择，并在六个数据集和三个基线模型上进行了全面的定量与定性分析及评估。结果表明，所提出的FS-BAN能持续提升基线模型的泛化性能，并在DG-FSC任务中达到最先进准确率。项目页面：https://yunqing-me.github.io/Born-Again-FS/。