An introduction to domain adaptation and transfer learning

Understand

In machine learning, if the training data is an unbiased sample of an underlying distribution, then the learned classification function will make accurate predictions for new samples.

• However, if the training data is not an unbiased sample, then there will be differences between how the training data is distributed and how the test data is distributed.
• Standard classifiers cannot cope with changes in data distributions between training and test phases, and will not perform well.
• Domain adaptation and transfer learning are sub-fields within machine learning that are concerned with accounting for these types of changes.