Abstract:
This work proposes the dense adversarial transfer learning based on class-invariance, which is a novel, unsupervised, conditional adversarial domain adaptation approach. ...Show MoreMetadata
Abstract:
This work proposes the dense adversarial transfer learning based on class-invariance, which is a novel, unsupervised, 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 enhances the accuracy by approximately 3% to 5%, demonstrating the improvement provided by the proposed network towards unsupervised domain adaptation.
Published in: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 04-10 June 2023
Date Added to IEEE Xplore: 05 May 2023
ISBN Information: