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Transfer learning in ML refers to the general idea of taking a model trained for one domain and applying it to another. However this article seems to only focus
by almondai 10y ago
Transfer learning in ML refers to the general idea of taking a model trained for one domain and applying it to another. However this article seems to only focus on feature mapping, ie. breaking down images into features using hidden layers of ImageNet models. In this case, the pretrained model is only acting as a feature extractor because it is not trained to maximize the embedded distance between the classes you are trying to differentiate.
- sarthakjain 10y agoYour right the article doesn't do justice to all of the transfer learning techniques. I cut out large portions of the original draft for brevity. Will write a longer post referencing all possible techniques, hopefully it won't turn into a review paper.
- deleted 10y ago[deleted]