Research: Computer Vision

What makes ImageNet good for Transfer Learning?

This work provides an empirical investigation into the various facets of this question, such as looking at the importance of the amount of examples, number of classes, balance between images-per-class and classes, and the role of fine and coarse grained recognition. We pre-train CNN features on various subsets of the ImageNet dataset and evaluate transfer performance on a variety of standard vision tasks.