An In-Depth Analysis of Adversarial Discriminative Domain Adaptation for Digit Classification

TL;DR

This paper implements and analyzes Adversarial Discriminative Domain Adaptation (ADDA) for digit classification, demonstrating its effectiveness across various domain shifts and providing insights into its limitations.

Contribution

It replicates and extends ADDA experiments, analyzing performance across multiple domain shifts and offering qualitative insights into its limitations.

Findings

ADDA improves accuracy on certain domain shifts

Minimal impact on in-domain classification accuracy

Provides qualitative analysis of ADDA's limitations

Abstract

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 .