  Few-Shot Learning (FSL) aims to improve a model's generalization capability
in low data regimes. Recent FSL works have made steady progress via metric
learning, meta learning, representation learning, etc. However, FSL remains
challenging due to the following longstanding difficulties. 1) The seen and
unseen classes are disjoint, resulting in a distribution shift between training
and testing. 2) During testing, labeled data of previously unseen classes is
sparse, making it difficult to reliably extrapolate from labeled support
examples to unlabeled query examples. To tackle the first challenge, we
introduce Hybrid Consistency Training to jointly leverage interpolation
consistency, including interpolating hidden features, that imposes linear
behavior locally and data augmentation consistency that learns robust
embeddings against sample variations. As for the second challenge, we use
unlabeled examples to iteratively normalize features and adapt prototypes, as
opposed to commonly used one-time update, for more reliable prototype-based
transductive inference. We show that our method generates a 2% to 5%
improvement over the state-of-the-art methods with similar backbones on five
FSL datasets and, more notably, a 7% to 8% improvement for more challenging
cross-domain FSL.
