Search Results for author: Jay Young

Found 1 papers, 1 papers with code

Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

1 code implementation IEEE Xplore: 2017 Nizar Massouh, Francesca Babiloni, Tatiana Tommasi, Jay Young, Nick Hawes, Barbara Caputo

We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy.

Object Categorization Object Discovery

Cannot find the paper you are looking for? You can Submit a new open access paper.