Striving for Simplicity: The All Convolutional Net

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Martin Riedmiller, Thomas Brox, Jost Tobias Springenberg, Alexey Dosovitskiy
Journal/Conference Name 3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings
Paper Category
Paper Abstract Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles Alternating convolution and max-pooling layers followed by a small number of fully connected layers. We re-evaluate the state of the art for object recognition from small images with convolutional networks, questioning the necessity of different components in the pipeline. We find that max-pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks. Following this finding -- and building on other recent work for finding simple network structures -- we propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets (CIFAR-10, CIFAR-100, ImageNet). To analyze the network we introduce a new variant of the "deconvolution approach" for visualizing features learned by CNNs, which can be applied to a broader range of network structures than existing approaches.
Date of publication 2014
Code Programming Language Multiple

Copyright Researcher 2022