Few-shot-learning (FSL) commonly requires a model to identify images (queries) that belong to classes unseen during training, based on a few labelled samples of the new classes (support set) as reference. As the test classes are novel, FSL is challenging with high generalization error with respect to the novel classes, where outliers query or support image during inference exacerbate the error further. So far, plenty of algorithms involve training data augmentation to improve the generalization capability of FSL models. In contrast, inspired by the fact that test samples are more relevant to the target domain, we believe that test-time augmentation may be more useful than training augmentation for FSL. In this work, to reduce the bias caused by unconventional test samples, we generate new test samples through combining them with similar train-class samples. Averaged representations of the test-time augmentation are then considered for few-shot classification. According to our experiments, by augmenting the support set and query with a few additional generated sample, we can achieve improvement for trained FSL models. Importantly, our method is universally compatible with different off-the-shelf FSL models, whose performance can be improved without extra dataset nor further training of the models themselves. Codes are available at https://github.com/WendyBaiYunwei/FSL-Rectifier.

翻译：小样本学习通常需要模型基于新类别中的少量标注样本（支持集）作为参考，识别训练期间未见类别中的图像（查询集）。由于测试类别具有新颖性，小样本学习面临高泛化误差的挑战，而推理过程中的异常查询或支持图像会进一步加剧该误差。截至目前，大量算法采用训练数据增广来提升小样本学习模型的泛化能力。相比之下，受测试样本与目标领域更相关的启发，我们认为测试时增广可能比训练增广对小样本学习更有效。本研究为减少非常规测试样本引起的偏差，通过将测试样本与相似的训练类样本结合，生成新的测试样本。随后采用测试时增广的平均表征进行小样本分类。实验表明，通过增加少量生成样本来增强支持集和查询集，可有效提升已训练的小样本学习模型性能。重要的是，本方法具有通用兼容性，无需额外数据集或模型再训练即可直接应用于不同现有小样本学习模型并提升其性能。代码开源于https://github.com/WendyBaiYunwei/FSL-Rectifier。