Literature DB >> 31394043

Deep Learning: The Good, the Bad, and the Ugly.

Thomas Serre1.   

Abstract

Artificial vision has often been described as one of the key remaining challenges to be solved before machines can act intelligently. Recent developments in a branch of machine learning known as deep learning have catalyzed impressive gains in machine vision-giving a sense that the problem of vision is getting closer to being solved. The goal of this review is to provide a comprehensive overview of recent deep learning developments and to critically assess actual progress toward achieving human-level visual intelligence. I discuss the implications of the successes and limitations of modern machine vision algorithms for biological vision and the prospect for neuroscience to inform the design of future artificial vision systems.

Entities:  

Keywords:  action recognition; artificial intelligence; computational neuroscience; deep learning; face recognition; image segmentation; neural networks; object recognition

Mesh:

Year:  2019        PMID: 31394043     DOI: 10.1146/annurev-vision-091718-014951

Source DB:  PubMed          Journal:  Annu Rev Vis Sci        ISSN: 2374-4642            Impact factor:   6.422


  26 in total

1.  Examining the Coding Strength of Object Identity and Nonidentity Features in Human Occipito-Temporal Cortex and Convolutional Neural Networks.

Authors:  Yaoda Xu; Maryam Vaziri-Pashkam
Journal:  J Neurosci       Date:  2021-03-31       Impact factor: 6.167

2.  Deep learning: Opening a third eye to myocardial perfusion imaging.

Authors:  Tomoe Hagio; Venkatesh L Murthy
Journal:  J Nucl Cardiol       Date:  2022-05-12       Impact factor: 5.952

3.  The contribution of object identity and configuration to scene representation in convolutional neural networks.

Authors:  Kevin Tang; Matthew Chin; Marvin Chun; Yaoda Xu
Journal:  PLoS One       Date:  2022-06-28       Impact factor: 3.752

4.  Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception.

Authors:  K Vinken; X Boix; G Kreiman
Journal:  Sci Adv       Date:  2020-10-14       Impact factor: 14.136

5.  Performance vs. competence in human-machine comparisons.

Authors:  Chaz Firestone
Journal:  Proc Natl Acad Sci U S A       Date:  2020-10-13       Impact factor: 11.205

Review 6.  Beyond the feedforward sweep: feedback computations in the visual cortex.

Authors:  Gabriel Kreiman; Thomas Serre
Journal:  Ann N Y Acad Sci       Date:  2020-02-28       Impact factor: 5.691

7.  Deep learning approach for quantification of organelles and misfolded polypeptide delivery within degradative compartments.

Authors:  Diego Morone; Alessandro Marazza; Timothy J Bergmann; Maurizio Molinari
Journal:  Mol Biol Cell       Date:  2020-05-13       Impact factor: 4.138

8.  DNNBrain: A Unifying Toolbox for Mapping Deep Neural Networks and Brains.

Authors:  Xiayu Chen; Ming Zhou; Zhengxin Gong; Wei Xu; Xingyu Liu; Taicheng Huang; Zonglei Zhen; Jia Liu
Journal:  Front Comput Neurosci       Date:  2020-11-30       Impact factor: 2.380

9.  Scene wheels: Measuring perception and memory of real-world scenes with a continuous stimulus space.

Authors:  Gaeun Son; Dirk B Walther; Michael L Mack
Journal:  Behav Res Methods       Date:  2021-07-09

10.  Untangling the Animacy Organization of Occipitotemporal Cortex.

Authors:  J Brendan Ritchie; Astrid A Zeman; Joyce Bosmans; Shuo Sun; Kirsten Verhaegen; Hans P Op de Beeck
Journal:  J Neurosci       Date:  2021-07-06       Impact factor: 6.167

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