Fri, 15 June 2018
On a long car ride, Linhda and Kyle record a short episode. This discussion is about transfer learning, a technique using in machine learning to leverage training from one domain to have a head start learning in another domain.
Transfer learning has some obvious appealing features. Take the example of an image recognition problem. There are now many widely available models that do general image recognition. Detecting that an image contains a "sofa" is an impressive feat. However, for a furniture company interested in more specific details, this classifier is absurdly general. Should the furniture company build a massive corpus of tagged photos, effectively starting from scratch? Or is there a way they can transfer the learnings from the general task to the specific one.
A general definition of transfer learning in machine learning is the use of taking some or all aspects of a pre-trained model as the basis to begin training a new model which a specific and potentially limited dataset.