Explainable AI

Current research on explainability and interpretability of machine learning algorithms

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Striving for Simplicity: The All Convolutional Net

Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, Martin Riedmiller. 2014

[ArXiv]    

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.