Modified Distribution Alignment for Domain Adaptation with Pre-trained Inception ResNet

Youshan Zhang, Brian D. Davison

2019-04-04General Classification Classification Domain Adaptation

Abstract

Deep neural networks have been widely used in computer vision. There are several well trained deep neural networks for the ImageNet classification challenge, which has played a significant role in image recognition. However, little work has explored pre-trained neural networks for image recognition in domain adaption. In this paper, we are the first to extract better-represented features from a pre-trained Inception ResNet model for domain adaptation. We then present a modified distribution alignment method for classification using the extracted features. We test our model using three benchmark datasets (Office+Caltech-10, Office-31, and Office-Home). Extensive experiments demonstrate significant improvements (4.8%, 5.5%, and 10%) in classification accuracy over the state-of-the-art.

Results

Task	Dataset	Metric	Value	Model
Domain Adaptation	Office-31	Average Accuracy	89.8	MDAIR

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