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Powering Finetuning in Few-Shot Learning: Domain-Agnostic Bias Reduction with Selected Sampling
[article]
2022
arXiv
pre-print
In recent works, utilizing a deep network trained on meta-training set serves as a strong baseline in few-shot learning. In this paper, we move forward to refine novel-class features by finetuning a trained deep network. Finetuning is designed to focus on reducing biases in novel-class feature distributions, which we define as two aspects: class-agnostic and class-specific biases. Class-agnostic bias is defined as the distribution shifting introduced by domain difference, which we propose
arXiv:2204.03749v2
fatcat:is5ancke4fevpaupikxj3luqni