ADDA提升数字分类在跨域场景下的准确率，且对原域性能影响小。
An In-Depth Analysis of Adversarial Discriminative Domain Adaptation for Digit Classification
- 使用对抗性判别域适应方法减少源域与目标域差异。
- 在部分域偏移下准确率显著提升，原域性能基本不变。
- 分析了失败案例的可能原因，适合关注域适应的开发者参考。
域适应是当前研究热点，旨在提升模型在真实数据上的泛化能力。对抗性学习已成为深度神经网络图像分类中提升泛化性能的有力手段。本文实现了一种名为对抗性判别域适应（ADDA）的特定对抗学习技术，并复现了原始论文中的数字分类实验。通过考察更广泛的域偏移情况，我们对应用ADDA后的域内分类准确率进行了深入分析。结果表明，ADDA在某些域偏移下显著提升了准确率，同时对原域性能影响极小。此外，我们还提供了定性分析，并提出了对ADDA在表现不佳的域偏移中局限性的可能解释。代码已公开于 https://github.com/eugenechoi2004/COS429_FINAL。
原文摘要 · Abstract (English)
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 .
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