Material

This supplemental material introduces implementation details, additional comparison experiments, complete proofs and checklist of our proposed model. A.1 Implementation Details Network Architecture: Inspired by [33], we utilize a pre-trained ResNet-50 [20] as the feature extractor for object recognition tasks (i.e., Office-31 [22], Office-Caltech [18] and Office-Home [46]). The penultimate fully-connected layer is replaced with a bottleneck layer and a classifier with weight normalization. Batch normalization is employed to normalize the outputs of bottleneck layer. For digit recognition task (i.e., Digits-Five [41]), we utilize a variant of the LeNet [27] as the feature extractor and classifier.