Introduction

The Residual Transfer Network (RTN) is a framework designed for domain adaptation, which is the task of adapting a model trained on a source domain to perform well on a target domain with a different data distribution. RTN builds on the concept of residual learning and integrates techniques to reduce the domain gap between the source and target distributions.

Why is it used for Unsupervised DA ?

Residual Transfer Networks (RTNs) shine particularly in unsupervised domain adaptation (UDA) because they are designed to address the lack of labeled data in the target domain by leveraging labeled data from the source domain. Here’s why RTNs are most effective in unsupervised settings, compared to supervised or semi-supervised approaches:

Core Features of RTN

Residual Learning: Domain Adaptation Mechanisms: Adversarial Training: Task-Specific Output:

Models

Feature extractor

ResNet (Residual Network) can be used as the feature extractor in a Residual Transfer Network (RTN). In fact, ResNet is often preferred in domain adaptation tasks because:

Residual Module

The Residual Module in a Residual Transfer Network (RTN) is a key component designed to refine and adjust features extracted from the source domain, aligning them with the target domain. This approach leverages residual learning, making the adaptation process more efficient and focused.

Domain Discrepancy Minimization

Domain discrepancy minimization is a crucial step in domain adaptation tasks, including in pipelines like the Residual Transfer Network (RTN). It focuses on reducing the difference in data distributions between the source domain (labeled) and the target domain (unlabeled or sparsely labeled). By minimizing this discrepancy, the model learns domain-invariant features that generalize well across both domains.

The MMD loss is minimized during training to bring the two distributions closer:

MMD 2 = ∥ 1 𝑛 𝑠 ∑ 𝑖 = 1 𝑛 𝑠 𝜙 ( 𝑥 𝑖 𝑠 ) − 1 𝑛 𝑡 ∑ 𝑖 = 1 𝑛 𝑡 𝜙 ( 𝑥 𝑖 𝑡 ) ∥ 2

Training Progress

Here are the training results for 5 epochs, showing how the classification loss decreases while the MMD loss stabilizes.

Epoch	Classification Loss	MMD Loss
1	6308.9418	15.3575
2	3660.6271	2.8869
3	2920.0178	1.5874
4	2421.1814	1.2705
5	2039.2852	1.0331

Observations

✅ Classification loss is steadily decreasing – The model is learning effectively.
✅ MMD loss is stabilizing – Domain adaptation is improving.
✅ Feature alignment between source and target domains is progressing well.

This markdown format ensures readability while making the data easy to reference in a GitHub README. 🚀 Let me know if you want additional visualizations, like loss curves!