  In this paper, we propose a compact network called CUNet (compact
unsupervised network) to counter the image classification challenge. Different
from the traditional convolutional neural networks learning filters by the
time-consuming stochastic gradient descent, CUNet learns the filter bank from
diverse image patches with the simple K-means, which significantly avoids the
requirement of scarce labeled training images, reduces the training
consumption, and maintains the high discriminative ability. Besides, we propose
a new pooling method named weighted pooling considering the different weight
values of adjacent neurons, which helps to improve the robustness to small
image distortions. In the output layer, CUNet integrates the feature maps
gained in the last hidden layer, and straightforwardly computes histograms in
non-overlapped blocks. To reduce feature redundancy, we implement the
max-pooling operation on adjacent blocks to select the most competitive
features. Comprehensive experiments are conducted to demonstrate the
state-of-the-art classification performances with CUNet on CIFAR-10, STL-10,
MNIST and Caltech101 benchmark datasets.
