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Learned Gaussian ProtoNet for improved cross-domain few-shot classification and generalization

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Abstract

To imitate intelligent human behaviour, computer vision introduces a fundamental task called Few-Shot learning (FSL) that carries the promise of alleviating the need for exhaustively labeled data. Using prior knowledge few-shot learning aims to learn and generalize to novel tasks containing limited examples with supervised information. Although metric-based methods demonstrated promising performance but due to the large disparity of feature distributions across domains they often fail to generalize. In this work, we propose a learned Gaussian ProtoNet model for fine-grained few-shot classification via meta-learning for both in-domain and cross-domain scenarios. Gaussian ProtoNet encoder helps to map an image into an embedding vector and Gaussian covariance matrix predicts the confidence region about individual data points. Direction and class-dependent distance metrics are adopted to estimate the distances to distinct class prototypes. Feature-wise modulated layers are embedded in the encoder to augment the feature distribution of images. The learning-to-learn approach is adopted for fine-tuning the hyper-parameters of incorporated feature-wise modulated layers for better generalization on unseen domains. Experimental results justify that our proposed model performs better than many state-of-the-art models and feature-wise modulation improves the performance under domain shifts.

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Data Availibility Statement

All data generated or analysed during this study has been properly cited in this published article (see reference section). If found difficulty in finding the data links, same can be available from the corresponding author on reasonable request.

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  1. National Institute of Technology, Srinagar, India

    Nadeem Yousuf Khanday & Shabir Ahmad Sofi

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  1. Nadeem Yousuf Khanday
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Khanday, N.Y., Sofi, S.A. Learned Gaussian ProtoNet for improved cross-domain few-shot classification and generalization. Neural Comput & Applic 35, 3435–3448 (2023). https://doi.org/10.1007/s00521-022-07897-9

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