Guided Labeling using Convolutional Neural Networks

12/06/2017
by   Sebastian Stabinger, et al.
0

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.

READ FULL TEXT

page 4

page 5

page 6

research
08/02/2020

Semi-supervised deep learning based on label propagation in a 2D embedded space

While convolutional neural networks need large labeled sets for training...
research
03/13/2018

Using Convolutional Neural Networks for Determining Reticulocyte Percentage in Cats

Recent advances in artificial intelligence (AI), specifically in compute...
research
08/29/2023

Efficient labeling of solar flux evolution videos by a deep learning model

Machine learning (ML) is becoming a critical tool for interrogation of l...
research
04/11/2021

Print Error Detection using Convolutional Neural Networks

This paper discusses the need of an automated system for detecting print...
research
03/13/2018

Using Convolutional Neural Network for Determining Reticulocyte Percentage in Cats

Recent advances in artificial intelligence (AI), specifically in compute...
research
11/04/2016

RenderGAN: Generating Realistic Labeled Data

Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable perf...
research
03/19/2019

A semi-supervised deep learning algorithm for abnormal EEG identification

Systems that can automatically analyze EEG signals can aid neurologists ...

Please sign up or login with your details

Forgot password? Click here to reset