End-to-end Generative Zero-shot Learning via Few-shot Learning

• URL: http://arxiv.org/abs/2102.04379v1
• Date: Mon, 8 Feb 2021 17:35:37 GMT
• Title: End-to-end Generative Zero-shot Learning via Few-shot Learning
• Authors: Georgios Chochlakis, Efthymios Georgiou, Alexandros Potamianos
• Abstract summary: State-of-the-art approaches to Zero-Shot Learning (ZSL) train generative nets to synthesize examples conditioned on the provided metadata. We introduce an end-to-end generative ZSL framework that uses such an approach as a backbone and feeds its synthesized output to a Few-Shot Learning algorithm.
• Score: 76.9964261884635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contemporary state-of-the-art approaches to Zero-Shot Learning (ZSL) train generative nets to synthesize examples conditioned on the provided metadata. Thereafter, classifiers are trained on these synthetic data in a supervised manner. In this work, we introduce Z2FSL, an end-to-end generative ZSL framework that uses such an approach as a backbone and feeds its synthesized output to a Few-Shot Learning (FSL) algorithm. The two modules are trained jointly. Z2FSL solves the ZSL problem with a FSL algorithm, reducing, in effect, ZSL to FSL. A wide class of algorithms can be integrated within our framework. Our experimental results show consistent improvement over several baselines. The proposed method, evaluated across standard benchmarks, shows state-of-the-art or competitive performance in ZSL and Generalized ZSL tasks.

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