Transfer Learning (3): Domain Adaptation

Tue, 13 May 2025 09:00:00 +0000

Your autonomous-driving stack works perfectly on sunny California freeways. Then it rains in Seattle. Top-1 accuracy drops from 95% to 70%. The model did not get worse — the data distribution shifted, and your training set never told it what wet asphalt looks like at dusk.

This is the everyday problem of domain adaptation: you have abundant labelled data in one distribution (the source) and unlabelled data in another (the target), and you need the model to perform on the target. This article shows you how, from first-principles theory to a working DANN implementation.

Transfer Learning (1): Fundamentals and Core Concepts

Thu, 01 May 2025 09:00:00 +0000

You spent two weeks training an ImageNet classifier on a rack of GPUs. On Monday morning, your team lead asks for a chest X-ray pneumonia model, and the entire labeled dataset is two hundred images. Do you book another two weeks of GPU time and start from scratch?

Of course not. You use what the ImageNet model already knows about edges, textures, and shapes, swap out the last layer, and fine-tune on the X-rays. Two hours later, you have a model that beats anything you could have trained from random weights with so little data. That’s transfer learning, and it’s why most real-world deep learning projects ship in days instead of months.

Domain Adaptation on Chen Kai Blog

Transfer Learning (3): Domain Adaptation

Transfer Learning (1): Fundamentals and Core Concepts