Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural Networks

TL;DR

This paper introduces an evolutionary approach to deep neural network design, where networks evolve over generations through genetic-inspired processes, resulting in highly efficient architectures with state-of-the-art performance.

Contribution

It presents a novel evolutionary synthesis framework for deep neural networks, encoding architectures as synaptic probability models and evolving them over generations.

Findings

Evolved networks achieved state-of-the-art visual saliency performance.

Network architectures became significantly more efficient, with ~48-fold reduction in synapses.

The method successfully mimics biological evolution to optimize neural network design.

Abstract

Taking inspiration from biological evolution, we explore the idea of "Can deep neural networks evolve naturally over successive generations into highly efficient deep neural networks?" by introducing the notion of synthesizing new highly efficient, yet powerful deep neural networks over successive generations via an evolutionary process from ancestor deep neural networks. The architectural traits of ancestor deep neural networks are encoded using synaptic probability models, which can be viewed as the `DNA' of these networks. New descendant networks with differing network architectures are synthesized based on these synaptic probability models from the ancestor networks and computational environmental factor models, in a random manner to mimic heredity, natural selection, and random mutation. These offspring networks are then trained into fully functional networks, like one would train a newborn, and have more efficient, more diverse network architectures than their ancestor networks, while achieving powerful modeling capabilities. Experimental results for the task of visual saliency demonstrated that the synthesized `evolved' offspring networks can achieve state-of-the-art performance while having network architectures that are significantly more efficient (with a staggering $\sim$48-fold decrease in synapses by the fourth generation) compared to the original ancestor network.