  Supervised learning of convolutional neural networks (CNNs) can require very
large amounts of labeled data. Labeling thousands or millions of training
examples can be extremely time consuming and costly. One direction towards
addressing this problem is to create features from unlabeled data. In this
paper we propose a new method for training a CNN, with no need for labeled
instances. This method for unsupervised feature learning is then successfully
applied to a challenging object recognition task. The proposed algorithm is
relatively simple, but attains accuracy comparable to that of more
sophisticated methods. The proposed method is significantly easier to train,
compared to existing CNN methods, making fewer requirements on manually labeled
training data. It is also shown to be resistant to overfitting. We provide
results on some well-known datasets, namely STL-10, CIFAR-10, and CIFAR-100.
The results show that our method provides competitive performance compared with
existing alternative methods. Selective Convolutional Neural Network (S-CNN) is
a simple and fast algorithm, it introduces a new way to do unsupervised feature
learning, and it provides discriminative features which generalize well.
