Transfer learning, or inductive transfer, refers to the transfer of knowledge from a source task to a target task. In the context of convolutional neural networks (CNNs), transfer learning can be implemented by transplanting the learned feature layers from one CNN (derived from the source task) to initialize another (for the target task). Previous research has shown that the choice of the source CNN impacts the performance of the target task. In the current literature, there is no principled way for selecting a source CNN for a given target task despite the increasing availability of pre-trained source CNNs. In this paper we investigate the possibility of automatically ranking source CNNs prior to utilizing them for a target task. In particular, we present an information theoretic framework to understand the source-target relationship and use this as a basis to derive an approach to automatically rank source CNNs in an efficient, zeroshot manner. The practical utility of the approach is thoroughly evaluated using the Places-MIT dataset, MNIST dataset and a real-world MRI database. Experimental results demonstrate the efficacy of the proposed ranking method for transfer learning.
For example, an information theoretic framework was developed REF to rank source convolution neural networks (CNNs) and select the top-k CNNs for the target learning task by understanding the source-target relationship.
On automated source selection for transfer learning in convolutional neural networks
Pattern recognition
Pattern recognition
Medicine Computer Science