Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
This work addresses the scalability of brain-inspired learning methods for AI researchers, but it is incremental as it benchmarks existing algorithms without major breakthroughs.
The study evaluated biologically-motivated deep learning algorithms like target-propagation and feedback alignment on MNIST, CIFAR-10, and ImageNet, finding they performed well on MNIST but significantly worse than backpropagation on CIFAR and ImageNet, especially in locally-connected architectures.
The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks. Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance. We present results on the MNIST, CIFAR-10, and ImageNet datasets and explore variants of target-propagation (TP) and feedback alignment (FA) algorithms, and explore performance in both fully- and locally-connected architectures. We also introduce weight-transport-free variants of difference target propagation (DTP) modified to remove backpropagation from the penultimate layer. Many of these algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP and FA variants perform significantly worse than BP, especially for networks composed of locally connected units, opening questions about whether new architectures and algorithms are required to scale these approaches. Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.