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

arXiv: https://arxiv.org/abs/1904.02322 · HTML: https://mlsanity.com/paper/1904.02322/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [A Survey of Unsupervised Domain Adaptation for Visual Recognition](https://mlsanity.com/paper/2112.06745/) (2021)
- [Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2105.02089/) (2021)
- [Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation](https://mlsanity.com/paper/2104.13486/) (2021)
- [Deep Least Squares Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2111.02207/) (2021)
- [House Price Prediction Based On Deep Learning](https://mlsanity.com/paper/2204.09050/) (2022)

## Abstract (arXiv)

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.