Online Representation Learning with Multi-layer Hebbian Networks for Image Classification Tasks

By Yanis Bahroun, Andrea Soltoggio. 2017

3 citations recorded by the source corpus.

Unsupervised learning allows algorithms to adapt to different data thanks to the autonomous discovery of discriminating features during the training. When these algorithms are reducible to cost-function minimisation, better interpretations of their learning dynamics are possible. Recently, new Hebbian-like plasticity, bio-inspired, local and unsupervised learning rules for neural networks, have been shown to minimise a cost-function while performing online sparse representation learning. However, it is unclear to what degree such rules are effective to learn features from images. To investigate this point, this study introduces a novel multi-layer Hebbian network trained by a rule derived from a non-negative classical multidimensional scaling cost-function. The performance is compared to that of other fully unsupervised learning algorithms.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Those rules depend often on the precise topology of the network and are hand-designed, although cost-minimization Hebbian rules have been proposed recently (Pehlevan et al., 2015; Bahroun and Soltoggio, 2017).”