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

Youshan Zhang; Brian D. Davison

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arXiv · 1904.02322

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

Youshan Zhang·Brian D. Davison

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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.

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Youshan Zhang, Brian D. Davison. 2019-04-04. Modified Distribution Alignment for Domain Adaptation with Pre-trained Inception ResNet. https://arxiv.org/abs/1904.02322

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