Minyoung Huh

Minyoung Huh
Are you Minyoung Huh?

Claim your profile, edit publications, add additional information:

Contact Details

Name
Minyoung Huh
Affiliation
Location

Pubs By Year

Pub Categories

 
Computer Science - Learning (1)
 
Computer Science - Artificial Intelligence (1)
 
Computer Science - Computer Vision and Pattern Recognition (1)

Publications Authored By Minyoung Huh

The tremendous success of ImageNet-trained deep features on a wide range of transfer tasks begs the question: what are the properties of the ImageNet dataset that are critical for learning good, general-purpose features? This work provides an empirical investigation of various facets of this question: Is more pre-training data always better? How does feature quality depend on the number of training examples per class? Does adding more object classes improve performance? For the same data budget, how should the data be split into classes? Is fine-grained recognition necessary for learning good features? Given the same number of training classes, is it better to have coarse classes or fine-grained classes? Which is better: more classes or more examples per class? To answer these and related questions, we pre-trained CNN features on various subsets of the ImageNet dataset and evaluated transfer performance on PASCAL detection, PASCAL action classification, and SUN scene classification tasks. Our overall findings suggest that most changes in the choice of pre-training data long thought to be critical do not significantly affect transfer performance.? Given the same number of training classes, is it better to have coarse classes or fine-grained classes? Which is better: more classes or more examples per class? Read More