Improving the generalization performance of deep networks by dual pattern learning with adversarial adaptation

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In this paper, we present a dual pattern learning network architecture with adversarial adaptation (DPLAANet). Unlike conventional networks, the proposed network has two input branches and two loss functions. This architecture forces the network to learn robust features by analysing dual inputs. 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. In addition, we propose to associate the two input branches with two random interest values during training. As a stochastic regularization technique, this method can improve the generalization performance. Moreover, we introduce to use the adversarial training approach to reduce the domain difference between fused image features and single image features. Extensive experiments on CIFAR-10, CIFAR-100, FI-8, the Google commands dataset, and MNIST demonstrate that our DPLAANets exhibit better performance than the baseline networks. The experimental results on subsets of CIFAR-10, CIFAR-100, and MNIST demonstrate that DPLAANets have a good generalization performance on small datasets. The proposed architecture can be easily extended to have more than two input branches. The experimental results on subsets of MNIST show that the architecture with three branches outperforms two branches when the training set is extremely small.