Transfer Learning

Transfer learning or inductive transfer is a research problem in ML that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize Tigers could apply when trying to recognize Lions.

There are two major transfer learning scenarios in Neural network :

  • Finetuning the convnet: Instead of random initializaion, process initialize the network with a pretrained network.
  • ConvNet as fixed feature extractor: Here, we will freeze the weights for all of the network except that of the final fully connected layer. This last fully connected layer is replaced with a new one with random weights and only this layer is trained.

Using: Pytorch

Using: Tensorflow

Using: GIT

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