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I understand "domain adaptation" to be a type of "transfer-learning" technique.

Domain Adaptation: By applying knowledge obtained from a domain with sufficient teacher labels (Source Domain) to a target domain without sufficient information (Target Domain), a discriminator, etc. that works with high accuracy in the target domain is learned. (Domain is a term that refers to a collection of data.)

Can I use the "domain adaptation" approach for "fine-tuning" in object detection tasks?

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Yes. Fine-tuning is basically one very heuristic approach to doing domain adaptation. In particular, it's an (again, very heuristic) way to do supervised domain adaptation, i.e., where you have labeled data from the target domain. (There are also many unsupervised domain adaptation approaches.)

You may find these slides or this introduction to transfer learning and domain adaptation helpful.

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  • $\begingroup$ Thank you for your answer. For example, I want to train a model to detect Personal Protective Equipment (PPE). However, there are many different types of hardhats, gloves, and safety harnesses, plus a variety of workwear looks. It would be difficult to collect a large amount of diverse labeled data. In this case, I would collect a large amount of data and do labelling (or annotation). Should I then "transfer learning"? Could you give me some advice? $\endgroup$
    – lee_0213
    Nov 25, 2022 at 4:19

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