MINIMIZING DISTRIBUTION SHIFT BY USING THE DEEP ADVERSARIAL NEURAL NETWORK

Tóm tắt

Minimizing distribution shift is a critical challenge in domain adaptation (DA), as models trained on a source domain often experience degraded performance when applied to a different target domain. To address this issue, deep adversarial neural networks have emerged as a powerful approach to reduce domain discrepancies by leveraging adversarial learning. These networks employ a domain discriminator that encourages the feature extractor to learn domain-invariant representations, thereby aligning the distributions of source and target domains. By minimizing distribution shift, deep adversarial neural networks enable better generalization of deep learning models across diverse applications such as image classification, object recognition, semantic segmentation, and person re- identification. The integration of adversarial training with feature alignment techniques significantly improves model adaptability without requiring extensive labeled data in the target domain. However, challenges such as mode collapse, instability in adversarial training, and the selection of optimal feature representations remain key areas for further research. In this work, we explore deep adversarial neural networks as a solution for minimizing distribution shift and provide an in-depth analysis of their effectiveness, limitations, and potential improvements.