Transresnet: Transferable Resnet For Domain Adaptation

Although Deep Convolutional Neural Network (DCNN) has been admittedly witnessed as an enormous success in a wide range of applications, most of them require sufficient annotations with time-consuming and labor-exhausting efforts. Existing domain adaptation (DA) approaches delve into designing an effective loss module to minimize the distribution gap between the source and target domains. However, few studies pay attention to improve the backbone or network architecture for DA issues. In this paper, we propose a new backbone for DA specially, i.e., Transferable ResNet (TransResNet). TransResNet remedies the residual block in ResNet, separating source and target input features and highlighting more transferable channels in each block. It can be easily applied to all kinds of DA methods, without adding any extra learning parameters. We conduct substantial experiments on two general DA datasets and embed TransResNet into two seminal DA methods, including DANN and CDAN. Experimental results demonstrate TransResNet improves the transferability of the architecture, indicating that it is a great substitute for ResNet as a network backbone in DA issues.