An In-Depth Analysis of Adversarial Discriminative Domain Adaptation for Digit Classification
This is an incremental study that provides detailed analysis for researchers in domain adaptation, focusing on digit classification tasks.
The paper replicates Adversarial Discriminative Domain Adaptation (ADDA) for digit classification, analyzing its performance across various domain shifts and showing it significantly improves accuracy in some cases with minimal in-domain impact.
Domain adaptation is an active area of research driven by the growing demand for robust machine learning models that perform well on real-world data. Adversarial learning for deep neural networks (DNNs) has emerged as a promising approach to improving generalization ability, particularly for image classification. In this paper, we implement a specific adversarial learning technique known as Adversarial Discriminative Domain Adaptation (ADDA) and replicate digit classification experiments from the original ADDA paper. We extend their findings by examining a broader range of domain shifts and provide a detailed analysis of in-domain classification accuracy post-ADDA. Our results demonstrate that ADDA significantly improves accuracy across certain domain shifts with minimal impact on in-domain performance. Furthermore, we provide qualitative analysis and propose potential explanations for ADDA's limitations in less successful domain shifts. Code is at https://github.com/eugenechoi2004/COS429_FINAL .