Discriminative unsupervised feature learning with convolutional neural networks

NIPS2014

思路：

将unlabeled 图像进行旋转、平移、增强等操作，组成不同的数据集合（同一图像生成的图像为同一数据集合）。利用CNN 将不同数据集合区分开。

目标：

区分不同图像，保证平移等变换的鲁棒性。

网络结构：

small: two convolutional layers with 64 filters each followed by a fully connected layer with 128 neurons.

large: three convolutional layers with 64,128,256 filters  respectively followed by a fully connected layer with 512 neurons.

kernel size: 5*5

2*2 max pooling after the first two convolutional layers

dropout was applied to the fully connected layers

code and training data :