  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.
