Contrast sensitivity functions in autoencoders
Li, Qiang (Universitat de Valencia. Laboratori de Processament d'imatges)
Gómez Villa, Alexandra (Centre de Visió per Computador)
Bertalmío, Marcelo (Consejo Superior de Investigaciones Científicas. Instituto de Óptica)
Malo, Jesús (Universitat de Valencia. Laboratori de Processament d'imatges)

Data:	2022
Resum:	Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images. Here we reassess the relevance of low-level vision tasks in the explanation of the contrast sensitivity functions (CSFs) in light of 1) the current trend of using artificial neural networks for studying vision, and 2) the current knowledge of retinal image representations. As a first contribution, we show that a very popular type of convolutional neural networks (CNNs), called autoencoders, may develop human-like CSFs in the spatiotemporal and chromatic dimensions when trained to perform some basic low-level vision tasks (like retinal noise and optical blur removal), but not others (like chromatic) adaptation or pure reconstruction after simple bottlenecks). As an illustrative example, the best CNN (in the considered set of simple architectures for enhancement of the retinal signal) reproduces the CSFs with a root mean square error of 11% of the maximum sensitivity. As a second contribution, we provide experimental evidence of the fact that, for some functional goals (at low abstraction level), deeper CNNs that are better in reaching the quantitative goal are actually worse in replicating human-like phenomena (such as the CSFs). This low-level result (for the explored networks) is not necessarily in contradiction with other works that report advantages of deeper nets in modeling higher level vision goals. However, in line with a growing body of literature, our results suggests another word of caution about CNNs in vision science because the use of simplified units or unrealistic architectures in goal optimization may be a limitation for the modeling and understanding of human vision.
Ajuts:	Agencia Estatal de Investigación DPI2017-89867-C2-2-R Agencia Estatal de Investigación PID2020-118071GB-I00 Agencia Estatal de Investigación PGC2018-099651-B-I00
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Spatiotemporal and chromatic contrast sensitivity ; Convolutional autoencoders ; Modulation transfer function ; Noisy cones ; Deblurring and denoising ; Chromatic adaptation ; Natural images ; Statistical goals ; Architectures
Publicat a:	Journal of Vision, Vol. 22, Issue 6 (May 2022) , art. 8, ISSN 1534-7362

DOI: 10.1167/jov.22.6.8
PMID: 35587354

45 p, 19.6 MB

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