In inductive transfer learning, fine-tuning pretrained convolutional networks substantially outperforms training from scratch. When using finetuning, the underlying assumption is that the pretrained model extracts generic features, which are at least partially relevant for solving the target task, but would be difficult to extract from the limited amount of data available on the target task. However, besides the initialization with the pre-trained model and the early stopping, there is no mechanism in fine-tuning for retaining the features learned on the source task. In this paper, we investigate several regularization schemes that explicitly promote the similarity of the final solution with the initial model. We eventually recommend a simple L 2 penalty using the pre-trained model as a reference, and we show that this approach behaves much better than the standard scheme using weight decay on a partially frozen network.
Closely related to our work, Li et al. REF investigated several regularization schemes that explicitly promote the similarity of the fine-tuned model with the original pre-trained model.
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
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Mathematics Computer Science