Task Adaptive Feature Transformation for One-Shot Learning

• URL: http://arxiv.org/abs/2304.06832v1
• Date: Thu, 13 Apr 2023 21:52:51 GMT
• Title: Task Adaptive Feature Transformation for One-Shot Learning
• Authors: Imtiaz Masud Ziko, Freddy Lecue and Ismail Ben Ayed
• Abstract summary: We introduce a simple non-linear embedding adaptation layer, which is fine-tuned on top of fixed pre-trained features for one-shot tasks. We show consistent improvements over a variety of one-shot benchmarks, outperforming recent state-of-the-art methods.
• Score: 21.20683465652298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a simple non-linear embedding adaptation layer, which is fine-tuned on top of fixed pre-trained features for one-shot tasks, improving significantly transductive entropy-based inference for low-shot regimes. Our norm-induced transformation could be understood as a re-parametrization of the feature space to disentangle the representations of different classes in a task specific manner. It focuses on the relevant feature dimensions while hindering the effects of non-relevant dimensions that may cause overfitting in a one-shot setting. We also provide an interpretation of our proposed feature transformation in the basic case of few-shot inference with K-means clustering. Furthermore, we give an interesting bound-optimization link between K-means and entropy minimization. This emphasizes why our feature transformation is useful in the context of entropy minimization. We report comprehensive experiments, which show consistent improvements over a variety of one-shot benchmarks, outperforming recent state-of-the-art methods.

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