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

2025/10/14 by Aashish Dhawan, Dhawan, Aashish, Mudgal, Divyanshu
Computer Science · #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Speech Recognition and Synthesis

paper · pdf · doi:10.48550/arxiv.2510.12075

openalex publication_date 2025/10/14 · openalex created_date 2025/10/17 · openalex updated_date 2026/07/28

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

Citations

• Generative Adversarial Networks
• Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

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