PyTorch-Hebbian：在深度学习框架中促进本地学习
PyTorch-Hebbian: facilitating local learning in a deep learning framework
摘要：
最近，基于Hebb的突触功效的变化仅取决于突触前和突触后神经元的活动的想法的无监督局部学习已经显示出作为反向传播的替代训练机制的潜力。不幸的是，Hebbian学习仍然是实验性的，很少进入标准的深度学习框架。在这项工作中，我们研究了Hebbian学习在标准深度学习工作流程中的潜力。为此，提出了一个对现有深度学习管道中的局部学习规则进行彻底和系统评估的框架。使用这个框架，赫布学习图像分类的特征提取器的潜力进行了说明。特别是，该框架用于将Krotov-Hopfield学习规则扩展到标准卷积神经网络，而不会牺牲端到端反向传播的准确性。源代码可在https://github.com/Joxis/pytorch-hebbian上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
