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Found 5 papers, 1 papers with code
On Correlation of Features Extracted by Deep Neural Networks
It is shown that both network size and activation function are the two most important components that foster the tendency of DNNs to extract redundant features.
Diversity Regularized Adversarial Learning
In addition to the gradient information from the adversarial loss made available by the discriminator, diversity regularization also ensures that a more stable gradient is provided to update both the generator and discriminator.
Building Efficient ConvNets using Redundant Feature Pruning
This paper presents an efficient technique to prune deep and/or wide convolutional neural network models by eliminating redundant features (or filters).
Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data
Unsupervised feature extractors are known to perform an efficient and discriminative representation of data.
Deep Learning of Part-based Representation of Data Using Sparse Autoencoders with Nonnegativity Constraints
We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data.
