scholarly journals Eccentricity Dependent Deep Neural Networks for Modeling Human Vision

2017 ◽  
Vol 17 (10) ◽  
pp. 808 ◽  
Author(s):  
Gemma Roig ◽  
Francis Chen ◽  
Xavier Boix ◽  
Tomaso Poggio
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Human Vision

scholarly journals How well do Deep Neural Networks model Human Vision?

2016 ◽  
Vol 16 (12) ◽  
pp. 176
Author(s):  
John Clevenger ◽  
Diane Beck
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Human Vision

scholarly journals Noise-robust recognition of objects by humans and deep neural networks

2020 ◽  
Author(s):  
Hojin Jang ◽  
Devin McCormack ◽  
Frank Tong
Keyword(s):  
Neural Networks ◽  
Visual Processing ◽  
Deep Neural Networks ◽  
Signal To Noise Ratio ◽  
Human Vision ◽  
Training Procedure ◽  
Robust Recognition ◽  
Recognition Of Objects ◽  
Noise Robust ◽  
Level Performance

ABSTRACTDeep neural networks (DNNs) can accurately recognize objects in clear viewing conditions, leading to claims that they have attained or surpassed human-level performance. However, standard DNNs are severely impaired at recognizing objects in visual noise, whereas human vision remains robust. We developed a noise-training procedure, generating noisy images of objects with low signal-to-noise ratio, to investigate whether DNNs can acquire robustness that better matches human vision. After noise training, DNNs outperformed human observers while exhibiting more similar patterns of performance, and provided a better model for predicting human recognition thresholds on an image-by-image basis. Noise training also improved DNN recognition of vehicles in noisy weather. Layer-specific analyses revealed that the contaminating effects of noise were dampened, rather than amplified, across successive stages of the noise-trained network, with greater benefit at higher levels of the network. Our findings indicate that DNNs can learn noise-robust representations that better approximate human visual processing.

scholarly journals Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging images

PLoS Biology ◽  
2021 ◽  
Vol 19 (12) ◽  
pp. e3001418
Author(s):  
Hojin Jang ◽  
Devin McCormack ◽  
Frank Tong
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Functional Neuroimaging ◽  
Visual Learning ◽  
Human Vision ◽  
Noise Robustness ◽  
Training Procedure ◽  
Correlated Noise ◽  
Neural Responses ◽  
Viable Model

Deep neural networks (DNNs) for object classification have been argued to provide the most promising model of the visual system, accompanied by claims that they have attained or even surpassed human-level performance. Here, we evaluated whether DNNs provide a viable model of human vision when tested with challenging noisy images of objects, sometimes presented at the very limits of visibility. We show that popular state-of-the-art DNNs perform in a qualitatively different manner than humans—they are unusually susceptible to spatially uncorrelated white noise and less impaired by spatially correlated noise. We implemented a noise training procedure to determine whether noise-trained DNNs exhibit more robust responses that better match human behavioral and neural performance. We found that noise-trained DNNs provide a better qualitative match to human performance; moreover, they reliably predict human recognition thresholds on an image-by-image basis. Functional neuroimaging revealed that noise-trained DNNs provide a better correspondence to the pattern-specific neural representations found in both early visual areas and high-level object areas. A layer-specific analysis of the DNNs indicated that noise training led to broad-ranging modifications throughout the network, with greater benefits of noise robustness accruing in progressively higher layers. Our findings demonstrate that noise-trained DNNs provide a viable model to account for human behavioral and neural responses to objects in challenging noisy viewing conditions. Further, they suggest that robustness to noise may be acquired through a process of visual learning.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks
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