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Found 4 papers, 2 papers with code
Robust Convolutional Neural Networks under Adversarial Noise
Recent studies have shown that Convolutional Neural Networks (CNNs) are vulnerable to a small perturbation of input called "adversarial examples".
Convolutional Clustering for Unsupervised Learning
In this work, we propose to train a deep convolutional network based on an enhanced version of the k-means clustering algorithm, which reduces the number of correlated parameters in the form of similar filters, and thus increases test categorization accuracy.
Ranked #69 on
Image Classification
on MNIST
Flattened Convolutional Neural Networks for Feedforward Acceleration
We present flattened convolutional neural networks that are designed for fast feedforward execution.
An Analysis of the Connections Between Layers of Deep Neural Networks
On the other hand, in unsupervised learning, one cannot rely on back-propagation techniques to learn the connections between layers.
