Towards Zero-Shot Learning with Fewer Seen Class Examples

• URL: http://arxiv.org/abs/2011.07279v1
• Date: Sat, 14 Nov 2020 11:58:35 GMT
• Title: Towards Zero-Shot Learning with Fewer Seen Class Examples
• Authors: Vinay Kumar Verma, Ashish Mishra, Anubha Pandey, Hema A. Murthy and Piyush Rai
• Abstract summary: We present a meta-learning based generative model for zero-shot learning (ZSL) This setup contrasts with the conventional ZSL approaches, where training typically assumes the availability of a sufficiently large number of training examples from each of the seen classes. We conduct extensive experiments and ablation studies on four benchmark datasets of ZSL and observe that the proposed model outperforms state-of-the-art approaches by a significant margin when the number of examples per seen class is very small.
• Score: 41.751885300474925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a meta-learning based generative model for zero-shot learning (ZSL) towards a challenging setting when the number of training examples from each \emph{seen} class is very few. This setup contrasts with the conventional ZSL approaches, where training typically assumes the availability of a sufficiently large number of training examples from each of the seen classes. The proposed approach leverages meta-learning to train a deep generative model that integrates variational autoencoder and generative adversarial networks. We propose a novel task distribution where meta-train and meta-validation classes are disjoint to simulate the ZSL behaviour in training. Once trained, the model can generate synthetic examples from seen and unseen classes. Synthesize samples can then be used to train the ZSL framework in a supervised manner. The meta-learner enables our model to generates high-fidelity samples using only a small number of training examples from seen classes. We conduct extensive experiments and ablation studies on four benchmark datasets of ZSL and observe that the proposed model outperforms state-of-the-art approaches by a significant margin when the number of examples per seen class is very small.

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