ViT-ProtoNet, a Prototypical Network with a ViT-Small encoder, is reported to reach 95-97% 5-shot accuracy on three benchmarks and 81.88% on FC100, but the evaluation lacks critical baselines.
Generalized Adaptation for Few-Shot Learning
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abstract
Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model. In this paper, we propose to use closed-form base learner, which constrains the adapting stage with pre-trained base model to get better generalized novel model. Following theoretical analysis proves its rationality as well as indication of how to train a well-generalized base model. We then conduct experiments on four benchmarks and achieve state-of-the-art performance in all cases. Notably, we achieve the accuracy of 87.75% on 5-shot miniImageNet which approximately outperforms existing methods by 10%.
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ViT-ProtoNet for Few-Shot Image Classification: A Multi-Benchmark Evaluation
ViT-ProtoNet, a Prototypical Network with a ViT-Small encoder, is reported to reach 95-97% 5-shot accuracy on three benchmarks and 81.88% on FC100, but the evaluation lacks critical baselines.