We are very excited to join forces with OctoML.ai! Contact Grigori Fursin for more details!

25 years of CNNs: Can we compare to human abstraction capabilities?

lib:355ed225f44e708a (v1.0.0)

Authors: Sebastian Stabinger,Antonio Rodríguez-Sánchez,Justus Piater
ArXiv: 1607.08366
Document:  PDF  DOI 
Abstract URL: http://arxiv.org/abs/1607.08366v1

We try to determine the progress made by convolutional neural networks over the past 25 years in classifying images into abstractc lasses. For this purpose we compare the performance of LeNet to that of GoogLeNet at classifying randomly generated images which are differentiated by an abstract property (e.g., one class contains two objects of the same size, the other class two objects of different sizes). Our results show that there is still work to do in order to solve vision problems humans are able to solve without much difficulty.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives


Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!