Guided Labeling using Convolutional Neural Networks
This addresses the labeling bottleneck for researchers and practitioners in computer vision, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of needing large labeled datasets for supervised deep learning by proposing guided labeling, a method to automatically select which unlabeled samples to label, which reduces the amount of labeling required compared to arbitrary labeling.
Over the last couple of years, deep learning and especially convolutional neural networks have become one of the work horses of computer vision. One limiting factor for the applicability of supervised deep learning to more areas is the need for large, manually labeled datasets. In this paper we propose an easy to implement method we call guided labeling, which automatically determines which samples from an unlabeled dataset should be labeled. We show that using this procedure, the amount of samples that need to be labeled is reduced considerably in comparison to labeling images arbitrarily.