Search Results for author:
Found 2 papers, 1 papers with code
Training Convolutional Networks with Web Images
We replicate the ImageNet large scale database (ILSVRC-2012) from images collected from the web using 4 different download strategies varying: the search engine, the query and the image resolution.
Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
