Do We Need More Training Data?

Understand

Datasets for training object recognition systems are steadily increasing in size.

• This paper investigates the question of whether existing detectors will continue to improve as data grows, or saturate in performance due to limited model complexity and the Bayes risk associated with the feature spaces in which they operate.
• We focus on the popular paradigm of discriminatively trained templates defined on oriented gradient features.
• We investigate the performance of mixtures of templates as the number of mixture components and the amount of training data grows.