October 26, 2019
On Network Design Spaces for Visual Recognition
International Conference on Computer Vision (ICCV)
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.
By: Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, Piotr Dollar
Facebook AI Research