Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification

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We propose unsupervised embedding adaptation for the downstream few-shot classification task. Based on findings that deep neural networks learn to generalize before memorizing, we develop Early-Stage Feature Reconstruction (ESFR) -- a novel adaptation scheme with feature reconstruction and dimensionality-driven early stopping that finds generalizable features. Incorporating ESFR consistently improves the performance of baseline methods on all standard settings, including the recently proposed transductive method. ESFR used in conjunction with the transductive method further achieves state-of-the-art performance on mini-ImageNet, tiered-ImageNet, and CUB; especially with 1.2%~2.0% improvements in accuracy over the previous best performing method on 1-shot setting.
Publisher
International Conference on Machine Learning (ICML)
Issue Date
2021-07-22
Language
English
Citation

Thirty-eighth International Conference on Machine Learning (ICML)

ISSN
2640-3498
URI
http://hdl.handle.net/10203/286837
Appears in Collection
EE-Conference Papers(학술회의논문)
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