DENSE ADVERSARIAL TRANSFER LEARNING BASED ON CLASS-INVARIANCE

This work proposes the dense adversarial transfer learning based on class-invariance, which is a novel, unsuper- vised, conditional adversarial domain adaptation approach. The proposed framework concatenates feature maps from the last layer of each backbone’s block to improve transfer learning; these features are weighted and densely connected to the features of each block along with the gradient-reversal layer. Classifiers are also added to the domain discriminators so that the network not only retains the classifying abilities when learning the domain- invariant features, but also has its domain adaptation abilities improved. In the experiment, the benchmark dataset Office- 31 is used to compare the performance of similar existing frameworks. In three transfer tasks, the proposed method en- hances the accuracy by approximately 3% to 5%, demonstrating the improvement provided by the proposed network towards unsupervised domain adaptation.