Guided Labeling using Convolutional Neural Networks

TL;DR

This paper introduces guided labeling, a method leveraging CNNs to select the most informative samples for labeling, significantly reducing manual labeling effort in supervised learning for computer vision.

Contribution

It presents a novel guided labeling approach that automatically identifies which unlabeled samples should be labeled, improving labeling efficiency.

Findings

Reduces the number of samples needing manual labeling

Automatically identifies informative samples for labeling

Enhances efficiency of supervised deep learning workflows

Abstract

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.