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arXiv:1607.08366cs.CV

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

Sebastian Stabinger, Antonio Rodríguez-Sánchez, Justus Piater

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Abstract

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.

arXiv comments
To appear in the proceedings of ICANN 2016, Springer
Journal reference
DOI
Subjects
cs.CVComputer Vision and Pattern Recognition

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  1. OpenAI o1 Results on ARC-AGI-Pub 1 comment

    • "Designed" is not right. What gives "AI models" (i.e. deep neural nets) a hard time is that there are very few examples in the public training and evaluation set: each task has three examples. So basically it's not a test of intelligence but a test of sample efficiency. Besides…

      YeGoblynQueenneView on HNParent context

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