Insights into neural architectures for learning numerical concepts from simple visual data

• Autorzy: Śluzek Andrzej

Identyfikatory

DOI

10.15439/2022F48

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

The paper reports some results on neural architectures for learning numerical concepts from visual data. We use datasets of small images with single-pixel dots (one to six per image) to learn the abstraction of small integers, and other numerical concepts (e.g. even versus odd numbers). Both fully-connected and convolutional architectures are investigated. The obtained results indicate that two categories of numerical properties apparently exist (in the context of discussed problems). In the first category, the properties can be learned without acquiring the counting skills, e.g. the notion of small, medium and large numbers. In the second category, explicit counting is embedded into the architecture so that the concepts are learned from numbers rather than directly from visual data. In general, we find that CNN architectures (if properly crafted) are more efficient in the discussed problems and (additionally) come with more plausible explainability.

Słowa kluczowe

EN

computer science; visualization; computer architecture; data visualization

PL

informatyka; wizualizacja; wizualizacja danych

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 205--209
• Opis fizyczny: Bibliogr. 12 poz., tab., wykr., il.

Twórcy

autor

Śluzek Andrzej

• Warsaw University of Life Sciences-SGGW ul. Nowoursynowska 166, 02-787 Warszawa, Poland

Bibliografia

• 1. A. J. Kersey and J. F. Cantlon, “Primitive concepts of number and the developing human brain,” Language Learning and Development, vol. 13, no. 2, pp. 191–214, 2017. http://dx.doi.org/10.1080/15475441.2016.1264878
• 2. M. H. Fischer and S. Shaki, “Number concepts: abstract and embodied,” Phil. Trans. Royal Society B, vol. 373, no. 1752, p. 20170125, 2018. http://dx.doi.org/10.1098/rstb.2017.0125
• 3. E. Walach and L. Wolf, “Learning to count with cnn boosting,” in Proceedings of the 14th European Conference on Computer Vision, part II, vol. LNCS 9906, 2016. http://dx.doi.org/10.1007/978-3-319-46475-6_41 pp. 660–676.
• 4. S. Sabathiel, J. L. McClelland, and T. Solstad, “Emerging representations for counting in a neural network agent interacting with a multimodal environment,” vol. ALIFE 2020: The 2020 Conference on Artificial Life, 2020. http://dx.doi.org/10.1162/isal_a_00333 pp. 736–743.
• 5. V. Troiani, J. E. Peelle, R. Clark, and M. Grossman, “Is it logical to count on quantifiers? dissociable neural networks underlying numerical and logical quantifiers,” Neuropsychologia, vol. 47, no. 1, pp. 104–111, 2009. http://dx.doi.org/https://doi.org/10.1016/j.neuropsychologia.2008.08.015
• 6. C. Creatore, S. Sabathiel, and T. Solstad, “Learning exact enumeration and approximate estimation in deep neural network models,” Cognition, vol. 215, p. 104815, 2021. http://dx.doi.org/10.1016/j.cognition.2021.104815
• 7. S. Guan and M. Loew, “Understanding the ability of deep neural networks to count connected components in images,” in 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2020. http://dx.doi.org/10.1109/AIPR50011.2020.9425331 pp. 1–7.
• 8. A. Śluzek, “Counting dots: On learning numerical concepts from visual data,” Proceedings of the 3rd Polish Conference on Artificial Intelligence, April 2022, Gdynia, Poland, pp. 16–19, 2022.
• 9. M. Fang, Z. Zhou, S. Chen, and J. L. McClelland, “Can a recurrent neural network learn to count things?” Cognitive Science, 2018.
• 10. A. Cope, E. Vasilaki, D. Minors, C. Sabo, J. Marshall, and A. Barron, “Abstract concept learning in a simple neural network inspired by the insect brain,” PLoS Computational Biology, vol. 14, no. 9, p. e1006435, 2018. http://dx.doi.org/10.1371/journal.pcbi.1006435
• 11. M. Tomonaga and T. Matsuzawa, “Enumeration of briefly presented items by the chimpanzee (pan troglodytes) and humans (homo sapiens),” Animal Learning & Behavior, vol. 30, p. 143âĂŞ157, 2002. http://dx.doi.org/10.3758/BF03192916
• 12. K. Wynn, “Children’s understanding of counting,” Cognition, vol. 36, no. 2, pp. 155–193, 1990. http://dx.doi.org/10.1016/0010-0277(90)90003-3

Uwagi

1. Short article

2. Track 3: 4th International Workshop on Artificial Intelligence in Machine Vision and Graphics

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).