Choice of the p-norm for high level classification features pruning in modern convolutional neural networks with local sensitivity analysis - Jeczmionek, Ernest; Kowalski, Piotr A. (Zielona Góra: Uniwersytet Zielonogórski)
Transfer learning has surfaced as a compelling technique in machine learning, enabling the transfer of knowledge across networks. This study evaluates the efficacy of ImageNet pretrained state-of-the-art networks, including DenseNet, ResNet, and VGG, in implementing transfer learning for prepruned models on compact datasets, such as FashionMNIST, CIFAR10, and CIFAR100. The primary objective is to reduce the number of neurons while preserving high-level features. To this end, local sensitivity analysis is employed alongside p-norms and various reduction levels.
Keywords: convolutional neural network (CNN); pruning; sensitivity analysis; transfer learning; ImageNet