# A Review on Domain Adaption and Generative Adversarial Networks(GANs)

Source: [A Review on Domain Adaption and Generative Adversarial Networks(GANs)](<https://www.sota2.com/research/paper/68fa7c1a1e209131cea8c543>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:56:14.357Z

Authors: Aashish Dhawan, Divyanshu Mudgal

Published: 2025-10-14T02:32:10.000Z

## Abstract

The major challenge in today's computer vision scenario is the availability of good quality labeled data. In a field of study like image classification, where data is of utmost importance, we need to find more reliable methods which can overcome the scarcity of data to produce results comparable to previous benchmark results. In most cases, obtaining labeled data is very difficult because of the high cost of human labor and in some cases impossible. The purpose of this paper is to discuss Domain Adaptation and various methods to implement it. The main idea is to use a model trained on a particular dataset to predict on data from a different domain of the same kind, for example - a model trained on paintings of airplanes predicting on real images of airplanes

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2510.12075>)

[PDF](<https://arxiv.org/pdf/2510.12075.pdf>)