# Generate To Adapt: Aligning Domains using Generative Adversarial Networks

## TL;DR

This paper introduces a novel domain adaptation method that uses a generative adversarial network to align source and target data distributions in a shared feature space, improving performance across various vision tasks.

## Contribution

It presents a new approach combining unsupervised learning and GANs for domain adaptation, demonstrating effectiveness across multiple datasets and tasks.

## Key findings

- Achieves state-of-the-art results on digit classification datasets.
- Performs well across different datasets like OFFICE and DIGITS.
- Outperforms previous methods in domain adaptation tasks.

## Abstract

Domain Adaptation is an actively researched problem in Computer Vision. In this work, we propose an approach that leverages unsupervised data to bring the source and target distributions closer in a learned joint feature space. We accomplish this by inducing a symbiotic relationship between the learned embedding and a generative adversarial network. This is in contrast to methods which use the adversarial framework for realistic data generation and retraining deep models with such data. We demonstrate the strength and generality of our approach by performing experiments on three different tasks with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain adaptation from synthetic to real data. Our method achieves state-of-the art performance in most experimental settings and by far the only GAN-based method that has been shown to work well across different datasets such as OFFICE and DIGITS.