Activation Learning by Local Competitions
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The backpropagation that drives the success of deep learning is most likely different from the learning mechanism of the brain. In this paper, we develop a biology-inspired learning rule that discovers features by local competitions among neurons, following the idea of Hebb's famous proposal. It is demonstrated that the unsupervised features learned by this local learning rule can serve as a pre-training model to improve the performance of some supervised learning tasks. More importantly, this local learning rule enables us to build a new learning paradigm very different from the backpropagation, named activation learning, where the output activation of the neural network roughly measures how probable the input patterns are. The activation learning is capable of learning plentiful local features from few shots of input patterns, and demonstrates significantly better performances than the backpropagation algorithm when the number of training samples is relatively small. This learning paradigm unifies unsupervised learning, supervised learning and generative models, and is also more secure against adversarial attack, paving a road to some possibilities of creating general-task neural networks.
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