“Net2Net: Accelerating Learning via Knowledge Transfer”, 2015-11-18 (; backlinks; similar):
We introduce techniques for rapidly transferring the information stored in one neural net into another neural net. The main purpose is to accelerate the training of a larger neural net.
During real-world workflows, one often trains very many different neural networks during the experimentation and design process. This is a wasteful process in which each new model is trained from scratch. Our Net2Net technique accelerates the experimentation process by instantaneously transferring the knowledge from a previous network to each new deeper or wider network.
Our techniques are based on the concept of function-preserving transformations between neural network specifications. This differs from previous approaches to pre-training that altered the function represented by a neural net when adding layers to it.
Using our knowledge transfer mechanism to add depth to Inception modules, we demonstrate a new state-of-the-art accuracy rating on the ImageNet dataset.