# On Minimum Discrepancy Estimation for Deep Domain Adaptation

## TL;DR

This paper introduces a novel unsupervised deep domain adaptation method that aligns second order statistics and maximum mean discrepancy within a two-stream CNN to improve image classification across different domains.

## Contribution

It proposes a new approach combining covariance alignment and MMD in a two-stream CNN for unsupervised domain adaptation, achieving state-of-the-art results.

## Key findings

- Achieves state-of-the-art performance on benchmark datasets
- Effective in handling domain shift in image classification
- Outperforms existing domain adaptation methods

## Abstract

In the presence of large sets of labeled data, Deep Learning (DL) has accomplished extraordinary triumphs in the avenue of computer vision, particularly in object classification and recognition tasks. However, DL cannot always perform well when the training and testing images come from different distributions or in the presence of domain shift between training and testing images. They also suffer in the absence of labeled input data. Domain adaptation (DA) methods have been proposed to make up the poor performance due to domain shift. In this paper, we present a new unsupervised deep domain adaptation method based on the alignment of second order statistics (covariances) as well as maximum mean discrepancy of the source and target data with a two stream Convolutional Neural Network (CNN). We demonstrate the ability of the proposed approach to achieve state-of the-art performance for image classification on three benchmark domain adaptation datasets: Office-31 [27], Office-Home [37] and Office-Caltech [8].