GradMax: Growing Neural Networks using Gradient Information

Utku Evci, Bart van Merrienboer, Thomas Unterthiner, Fabian Pedregosa, Max Vladymyrov

Keywords: efficient training, efficient, computer vision, architecture search

Abstract: The architecture and the parameters of neural networks are often optimized independently, which requires costly retraining of the parameters whenever the architecture is modified. In this work we instead focus on growing the architecture without requiring costly retraining. We present a method that adds new neurons during training without impacting what is already learned, while improving the training dynamics. We achieve the latter by maximizing the gradients of the new weights and efficiently find the optimal initialization by means of the singular value decomposition (SVD). We call this technique Gradient Maximizing Growth (GradMax) and demonstrate its effectiveness in variety of vision tasks and architectures. We open sourced our code at https://github.com/google-research/growneuron

One-sentence Summary: We present a method that adds new neurons during training without impacting what is already learned, while improving the training dynamics.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2201.05125/code)