Papers › On Network Design Spaces for Visual Recognition

On Network Design Spaces for Visual Recognition

30 May 2019ICCV 2019 10arXiv:1905.13214archive 2025-07-28

Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, Piotr Dollár

Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity. Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape. As a case study, we examine design spaces used in neural architecture search (NAS). We find significant statistical differences between recent NAS design space variants that have been largely overlooked. Furthermore, our analysis reveals that the design spaces for standard model families like ResNeXt can be comparable to the more complex ones used in recent NAS work. We hope these insights into distribution analysis will enable more robust progress toward discovering better networks for visual recognition.

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Edge-AI-Acceleration-Lab/RBFleX-NAS mentioned on GitHubpytorch report
facebookresearch/nds mentioned on GitHubMIT report
facebookresearch/pycls mentioned on GitHubpytorch report
feymanpriv/pymetric mentioned on GitHubpytorchMIT report
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Methods

1x1 ConvolutionAverage PoolingBatch NormalizationConvolutionGlobal Average PoolingGrouped ConvolutionKaiming InitializationLSTMReLUResNeXtResNeXt BlockResidual ConnectionSigmoid ActivationSoftmaxTanh Activation

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