Papers › Striving for Simplicity: The All Convolutional Net

Striving for Simplicity: The All Convolutional Net

21 Dec 2014arXiv:1412.6806archive 2025-07-28

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

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.

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AlexSzen/wwf_shark_id mentioned on GitHubpytorch report
Aniket84/Object-Recognition- mentioned on GitHub report
Gauravsharma-20/Minor-Project mentioned on GitHubtfMIT report
JonasWechsler/DeepLearningLab5 mentioned on GitHubtfMIT report
Kashi7/Object-Recognition mentioned on GitHub report
MisaOgura/flashtorch mentioned on GitHubpytorchMIT report
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ApplyCLAHE Gauravsharma-20/Minor-Project/preprocessing.py community (archive-listed) unverified MIT (permissive) · 3c9491e49880c0cd · report
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increase_brightness Gauravsharma-20/Minor-Project/kneeoa/Preprocessing/Trial.py community (archive-listed) unverified MIT (permissive) · 067757c707f0644d · report

Tasks

AllImage ClassificationObjectObject Recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Classification CIFAR-10 ACN Percentage correct 95.6 #128 of 265 Archive leaderboard report
Image Classification CIFAR-100 ACN Percentage correct 66.3 #189 of 211 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Convolution

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