  Motivated by the observation that humans can learn patterns from two given
images at one time, we propose a dual pattern learning network architecture in
this paper. Unlike conventional networks, the proposed architecture has two
input branches and two loss functions. Instead of minimizing the empirical risk
of a given dataset, dual pattern learning networks is trained by minimizing the
empirical dual prediction loss. We show that this can improve the performance
for single image classification. This architecture forces the network to learn
discriminative class-specific features by analyzing and comparing two input
images. In addition, the dual input structure allows the network to have a
considerably large number of image pairs, which can help address the
overfitting issue due to limited training data. Moreover, we propose to
associate each input branch with a random interest value for learning
corresponding image during training. This method can be seen as a stochastic
regularization technique, and can further lead to generalization performance
improvement. State-of-the-art deep networks can be adapted to dual pattern
learning networks without increasing the same number of parameters. Extensive
experiments on CIFAR-10, CIFAR- 100, FI-8, Google commands dataset, and MNIST
demonstrate that our DPLNets exhibit better performance than original networks.
The experimental results on subsets of CIFAR- 10, CIFAR-100, and MNIST
demonstrate that dual pattern learning networks have good generalization
performance on small datasets.
