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How does transfer learning work for regression tasks? Can someone point to an application where transfer learning has been successfully applied for regression tasks.

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    $\begingroup$ Do you need it to be regression to regression transfer learning or can be from classification to regression? The latter is quite common actually. $\endgroup$ Dec 7 '19 at 10:35
  • $\begingroup$ It would be great to know about both. If you can shed some light on either/both or point me to an application/papers that would be great. $\endgroup$
    – GSH
    Dec 8 '19 at 15:50
  • $\begingroup$ kaggle.com/c/diabetic-retinopathy-detection and kaggle.com/c/aptos2019-blindness-detection is one example of classification to regression. $\endgroup$ Dec 9 '19 at 3:18
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Consider the denoising autoencoder. The input features to a neural network are contaminated by a small amount of noise, sent through one or more intermediate layers, and then through a final layer of the same size as the input layer. This network is optimized to reconstruct the original data which can be seen as a form of regularization.

The resulting network weights (minus the final layer) are frozen and transferred to a supervised learning task (perhaps added to another neural network). It has been found that the features constructed by this process are quite valuable. This applies to regression or classification.

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    $\begingroup$ Could you add some references/links/examples to this answer? $\endgroup$ Aug 6 '20 at 16:45

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