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Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.
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For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks.
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This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited.
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From the practical standpoint, reusing or transferring information from previously learned tasks for the learning of new tasks has the potential to significantly improve the sample efficiency of a reinforcement learning agent.
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Andrew Ng said in his NIPS 2016 tutorial that TL will be the next driver of ML commercial success after supervised learning to highlight the importance of TL.
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In 2020 it was discovered that, due to their similar physical natures, transfer learning is possible between Electromyographic (EMG) signals from the muscles when classifying the behaviours of Electroencephalographic (EEG) brainwaves from the gesture recognition domain to the mental state recognition domain.
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It was also noted that this relationship worked vice-versa, showing that EEG can likewise be used to classify EMG in addition.
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The experiments noted that the accuracy of neural networks and convolutional neural networks were improved through transfer learning both at the first epoch (prior to any learning, ie. compared to standard random weight distribution) and at the asymptote (the end of the learning process).
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That is, algorithms are improved by exposure to another domain.
