In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch.
 When using fine-tuning, the underlying assumption is that the pre-trained 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.
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch. Addresses the problem of transfer learning in deep networks and proposes to have a regularization term that penalizes divergence from initialization. Proposes an analysis on different adaptive regularization techniques for deep transfer learning, specifically focusing on the use of an L@-SP condition