3 papers
cs.CV2021
Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic
Nidhi Gowdra, Roopak Sinha, Stephen MacDonell +1
Convolutional Neural Networks (CNNs) such as ResNet-50, DenseNet-40 and ResNeXt-56 are severely over-parameterized, necessitating a consequent increase in the computational resourc…
cs.CV2021
Examining and Mitigating Kernel Saturation in Convolutional Neural Networks using Negative Images
Nidhi Gowdra, Roopak Sinha, Stephen MacDonell
Neural saturation in Deep Neural Networks (DNNs) has been studied extensively, but remains relatively unexplored in Convolutional Neural Networks (CNNs). Understanding and alleviat…
cs.NE2021
Examining convolutional feature extraction using Maximum Entropy (ME) and Signal-to-Noise Ratio (SNR) for image classification
Nidhi Gowdra, Roopak Sinha, Stephen MacDonell
Convolutional Neural Networks (CNNs) specialize in feature extraction rather than function mapping. In doing so they form complex internal hierarchical feature representations, the…