  (Very early draft)Traditional supervised learning keeps pushing convolution
neural network(CNN) achieving state-of-art performance. However, lack of
large-scale annotation data is always a big problem due to the high cost of it,
even ImageNet dataset is over-fitted by complex models now. The success of
unsupervised learning method represented by the Bert model in natural language
processing(NLP) field shows its great potential. And it makes that unlimited
training samples becomes possible and the great universal generalization
ability changes NLP research direction directly. In this article, we purpose a
novel unsupervised learning method based on contrastive predictive coding.
Under that, we are able to train model with any non-annotation images and
improve model's performance to reach state-of-art performance at the same level
of model complexity. Beside that, since the number of training images could be
unlimited amplification, an universal large-scale pre-trained computer vision
model is possible in the future.
