Hebbian Semi-Supervised Learning in a Sample Efficiency Setting

@article{Lagani2021HebbianSL,
  title={Hebbian Semi-Supervised Learning in a Sample Efficiency Setting},
  author={Gabriele Lagani and F. Falchi and Claudio Gennaro and Giuseppe Amato},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2021},
  volume={143},
  pages={
          719-731
        }
}
  • Gabriele Lagani, F. Falchi, +1 author G. Amato
  • Published 16 March 2021
  • Computer Science, Medicine
  • Neural networks : the official journal of the International Neural Network Society
We propose to address the issue of sample efficiency, in Deep Convolutional Neural Networks (DCNN), with a semi-supervised training strategy that combines Hebbian learning with gradient descent: all internal layers (both convolutional and fully connected) are pre-trained using an unsupervised approach based on Hebbian learning, and the last fully connected layer (the classification layer) is trained using Stochastic Gradient Descent (SGD). In fact, as Hebbian learning is an unsupervised… 

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