🤖 AI Summary
To address performance degradation in cross-domain computer vision tasks caused by scarce labeled data in the target domain, this paper presents a systematic review of state-of-the-art domain adaptation (DA) methods, with emphasis on generative adversarial network (GAN)-driven feature alignment and distribution matching. We propose a GAN-based DA framework that integrates deep adversarial learning with discriminative feature representation learning; a shared discriminator guides alignment between source and target feature spaces without requiring target-domain labels, thereby mitigating domain shift. Extensive experiments on standard cross-domain image classification benchmarks—including Office-31 and ImageCLEF-DA—demonstrate that our approach achieves classification accuracy approaching fully supervised baselines and significantly outperforms conventional non-adversarial DA methods. This work advances robust, scalable cross-domain modeling under low-resource conditions, offering a principled solution for label-efficient adaptation in real-world deployment scenarios.
📝 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