PyTorch-Hebbian: facilitating local learning in a deep learning framework
This work addresses the problem of making Hebbian learning more accessible and practical for researchers in machine learning, though it is incremental as it builds on existing local learning concepts.
The authors tackled the challenge of integrating Hebbian learning into standard deep learning workflows by proposing a framework for systematic evaluation, and they demonstrated its potential by extending the Krotov-Hopfield rule to convolutional neural networks without accuracy loss compared to backpropagation.
Recently, unsupervised local learning, based on Hebb's idea that change in synaptic efficacy depends on the activity of the pre- and postsynaptic neuron only, has shown potential as an alternative training mechanism to backpropagation. Unfortunately, Hebbian learning remains experimental and rarely makes it way into standard deep learning frameworks. In this work, we investigate the potential of Hebbian learning in the context of standard deep learning workflows. To this end, a framework for thorough and systematic evaluation of local learning rules in existing deep learning pipelines is proposed. Using this framework, the potential of Hebbian learned feature extractors for image classification is illustrated. In particular, the framework is used to expand the Krotov-Hopfield learning rule to standard convolutional neural networks without sacrificing accuracy compared to end-to-end backpropagation. The source code is available at https://github.com/Joxis/pytorch-hebbian.