Flatness Improves Backbone Generalisation in Few-shot Classification
Rui Li Martin Trapp Marcus Klasson Arno Solin
Deployment of deep neural networks in real-world settings typically requires
adaptation to new tasks with few examples. Few-shot classification (FSC)
provides a solution to this problem by leveraging pre-trained backbones for
fast adaptation to new classes. Surprisingly, most efforts have only focused on
developing architectures for easing the adaptation to the target domain without
considering the importance of backbone training for good generalisation. We
show that flatness-aware backbone training with vanilla fine-tuning results in
a simpler yet competitive baseline compared to the state-of-the-art. Our
results indicate that for in- and cross-domain FSC, backbone training is
crucial to achieving good generalisation across different adaptation methods.
We advocate more care should be taken when training these models.