  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.
