caozhangjie
commented
6 years ago Yes, you can. But add more annotated data is usually better than using unsupervised target domain with domain adaptation. But if you use domain adaptation, you only need to annotate a little data to achieve comparable performance.
liangzimei
Hi, it‘’s my first time to try domain adaptation. Here is my scenario: i am doing a image classification task, there are already 100k training data with labels (called A), i also can obtain large data with no labels (called B) . Data A and B's domain shift are small. The plan i choose now is using data A to train a model to predict B's data directly. the results is also good. when i annotating more data from B, and use them and data A together to train, the results are better. However, to reduce the work of annotating images, my question is can i treat A as source, B as target to improve accuracy further (i.e., adding more data B in training phase compared with current plan) .