On Network Design Spaces for Visual Recognition

Understand

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.