Most existing works in few-shot learning rely on meta-learning the network on a large base dataset which is typically from the same domain as the target dataset. We tackle the problem of cross-domain few-shot learning where there is a large shift between the base and target domain. The problem of cross-domain few-shot recognition with unlabeled target data is largely unaddressed in the literature. STARTUP was the first method that tackles this problem using self-training. However, it uses a fixed teacher pretrained on a labeled base dataset to create soft labels for the unlabeled target samples. As the base dataset and unlabeled dataset are from different domains, projecting the target images in the class-domain of the base dataset with a fixed pretrained model might be sub-optimal. We propose a simple dynamic distillation-based approach to facilitate unlabeled images from the novel/base dataset. We impose consistency regularization by calculating predictions from the weakly-augmented versions of the unlabeled images from a teacher network and matching it with the strongly augmented versions of the same images from a student network. The parameters of the teacher network are updated as exponential moving average of the parameters of the student network. We show that the proposed network learns representation that can be easily adapted to the target domain even though it has not been trained with target-specific classes during the pretraining phase. Our model outperforms the current state-of-the art method by 4.4% for 1-shot and 3.6% for 5-shot classification in the BSCD-FSL benchmark, and also shows competitive performance on traditional in-domain few-shot learning task.
翻译:少见学习中的大多数现有工作都依赖于在大型基准数据集上对网络进行元化学习,而该数据库通常是来自目标数据集的同一领域。我们处理在基准域和目标域之间发生巨大变化的情况下交叉域域略图学习的问题。在文献中基本上没有解决使用未贴标签的目标数据交叉域略图识别的问题。STARTUP是第一个通过自我培训来解决这一问题的方法。然而,它使用固定的教师在标签基准数据集上预先训练,为未贴标签的目标样本创建软的正本分类标签。由于基础数据集和未贴标签的数据集来自不同的领域,我们用固定预设的预设模型模型在基础数据集的类域域域域间进行跨域略取目标图像的问题。我们提出了简单的动态蒸馏法方法,以便利从新/基准数据集中未贴标签的图像。我们通过一个教师网络的微缩略图版本来调整一致性,并且将它与当前正缩缩略图的版本相匹配,因此在学生网络中学习了1个方向网络的升级的B级参数。在学生网络中学习了1个方向上,我们提议的B的模型。