CSNNs: Unsupervised, Backpropagation-free Convolutional Neural Networks
for Representation Learning
- URL: http://arxiv.org/abs/2001.10388v2
- Date: Wed, 29 Jan 2020 10:47:08 GMT
- Title: CSNNs: Unsupervised, Backpropagation-free Convolutional Neural Networks
for Representation Learning
- Authors: Bonifaz Stuhr and J\"urgen Brauer
- Abstract summary: This work combines Convolutional Neural Networks (CNNs), clustering via Self-Organizing Maps (SOMs) and Hebbian Learning to propose the building blocks of Convolutional Self-Organizing Neural Networks (CSNNs)
Our approach replaces the learning of traditional convolutional layers from CNNs with the competitive learning procedure of SOMs and simultaneously learns local masks between those layers with separate Hebbian-like learning rules to overcome the problem of disentangling factors of variation when filters are learned through clustering.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This work combines Convolutional Neural Networks (CNNs), clustering via
Self-Organizing Maps (SOMs) and Hebbian Learning to propose the building blocks
of Convolutional Self-Organizing Neural Networks (CSNNs), which learn
representations in an unsupervised and Backpropagation-free manner. Our
approach replaces the learning of traditional convolutional layers from CNNs
with the competitive learning procedure of SOMs and simultaneously learns local
masks between those layers with separate Hebbian-like learning rules to
overcome the problem of disentangling factors of variation when filters are
learned through clustering. We investigate the learned representation by
designing two simple models with our building blocks, achieving comparable
performance to many methods which use Backpropagation, while we reach
comparable performance on Cifar10 and give baseline performances on Cifar100,
Tiny ImageNet and a small subset of ImageNet for Backpropagation-free methods.
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