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Gradient target propagation
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We report a learning rule for neural networks that computes how much each neuron should contribute to minimize a giving cost function via the estimation of its target value. By theoretical analysis, we show that this learning rule contains backpropagation, Hebian learning, and additional terms. We also give a general technique for weights initialization. Our results are at least as good as those obtained with backpropagation. The neural networks are trained and tested in three problems: MNIST, MNIST-Fashion, and CIFAR-10 datasets. The associated code is available at https://github.com/tiago939/target.
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Cited by 1 Pith paper
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The HSIC Bottleneck: Deep Learning without Back-Propagation
An HSIC-based information-bottleneck objective trains deep networks layer-by-layer without backpropagation and matches backpropagation accuracy on small image benchmarks in the reported runs.
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