Pairwise Generalization Network for Cross-Domain Image Recognition

In recent years, convolutional neural networks have received increasing attention from the computer vision and machine learning communities. Due to the differences in the distribution, tone and brightness of the training domain and test domain, researchers begin to focus on cross-domain image recognition. In this paper, we propose a Pairwise Generalization Network (PGN) for addressing the problem of cross-domain image recognition where Instance Normalization and Batch Normalization are added to enhance their abilities in the original domain and to expand to the new domain. Meanwhile, the Siamese architecture is utilized in the PGN to learn an embedding subspace that is discriminative, and map positive sample pairs aligned and negative sample pairs separated, which can work well even with only few labeled target data samples. We also add residual architecture and MMD loss for the PGN model to further improve its performance. Extensive experiments on two different public benchmarks show that our PGN solution significantly outperforms the state-of-the-art methods.