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Transfer Learning
- Train on a base network, try to take that network and tweak it to work for a new target network.
- Notes from CS231N.
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Tries to figure out how much information can we transfer between networks trained on different datasets.
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Quantifies the transferability by layer.
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Hypothesis:
- First few layers are general (Gabor Filters kind of features) and can adapt well.