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arXiv · 1802.03499

Local Contrast Learning

Chuanyun Xu·Yang Zhang·Xin Feng·YongXing Ge·Yihao Zhang·Jianwu Long
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Abstract

Learning a deep model from small data is yet an opening and challenging problem. We focus on one-shot classification by deep learning approach based on a small quantity of training samples. We proposed a novel deep learning approach named Local Contrast Learning (LCL) based on the key insight about a human cognitive behavior that human recognizes the objects in a specific context by contrasting the objects in the context or in her/his memory. LCL is used to train a deep model that can contrast the recognizing sample with a couple of contrastive samples randomly drawn and shuffled. On one-shot classification task on Omniglot, the deep model based LCL with 122 layers and 1.94 millions of parameters, which was trained on a tiny dataset with only 60 classes and 20 samples per class, achieved the accuracy 97.99% that outperforms human and state-of-the-art established by Bayesian Program Learning (BPL) trained on 964 classes. LCL is a fundamental idea which can be applied to alleviate parametric model's overfitting resulted by lack of training samples.

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Chuanyun Xu, Yang Zhang, Xin Feng, YongXing Ge, Yihao Zhang, Jianwu Long. 2018-02-10. Local Contrast Learning. https://arxiv.org/abs/1802.03499

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