Does anyone know a paper or code that does "unsupervised domain adaptation" for regression task? I saw most of the papers were benchmarked on classification tasks, not regression. I want to do something like training a model to predict a scalar value from an image (e.g. predicting image of a road to steering wheel angle for a self-driving car). One of the examples could be training on synthesis data from simulated environment (think GTA) and then trying to predict on real-world.

Here is one of the examples of unsupervised domain adaptation algorithm that also has an easy-to-access code with Keras: https://github.com/bbdamodaran/deepJDOT But it's for classification. The author said it can be used for regression but I had to change it. I changed it and it didn't work well so I don't know if it's my fault or the algo is not good for regression. I want to see papers that were benchmarked on regression so I know how well it performs on regression.

My real use case is to predict facial expression as a value from 0 to 1 like how open is the mouth. The source domain and target domain are real-world images but from different lighting.

Any suggestions are appreciated.

  • $\begingroup$ DeepJDOT is now working well. I have contributed to the author's code and now it's working for regression as well. $\endgroup$ – off99555 Jul 20 '19 at 4:22

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