scholarly journals Visual pathways from the perspective of cost functions and multi-task deep neural networks

Cortex ◽  
2018 ◽  
Vol 98 ◽  
pp. 249-261 ◽  
Author(s):  
H. Steven Scholte ◽  
Max M. Losch ◽  
Kandan Ramakrishnan ◽  
Edward H.F. de Haan ◽  
Sander M. Bohte
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Visual Pathways ◽  
Cost Functions

scholarly journals Visual pathways from the perspective of cost functions and multi-task deep neural networks

2017 ◽  
Author(s):  
H. Steven Scholte ◽  
Max M. Losch ◽  
Kandan Ramakrishnan ◽  
Edward H.F. de Haan ◽  
Sander M. Bohte
Keyword(s):  
Neural Networks ◽  
Visual System ◽  
Deep Neural Networks ◽  
Functional Organization ◽  
Feature Representation ◽  
Cost Functions ◽  
Dorsal Pathway ◽  
Vision For Action ◽  
Ventral Pathway ◽  
High Degree

AbstractVision research has been shaped by the seminal insight that we can understand the higher-tier visual cortex from the perspective of multiple functional pathways with different goals. In this paper, we try to give a computational account of the functional organization of this system by reasoning from the perspective of multi-task deep neural networks. Machine learning has shown that tasks become easier to solve when they are decomposed into subtasks with their own cost function. We hypothesize that the visual system optimizes multiple cost functions of unrelated tasks and this causes the emergence of a ventral pathway dedicated to vision for perception, and a dorsal pathway dedicated to vision for action. To evaluate the functional organization in multi-task deep neural networks, we propose a method that measures the contribution of a unit towards each task, applying it to two networks that have been trained on either two related or two unrelated tasks, using an identical stimulus set. Results show that the network trained on the unrelated tasks shows a decreasing degree of feature representation sharing towards higher-tier layers while the network trained on related tasks uniformly shows high degree of sharing. We conjecture that the method we propose can be used to analyze the anatomical and functional organization of the visual system and beyond. We predict that the degree to which tasks are related is a good descriptor of the degree to which they share downstream cortical-units.

Multiobjective optimization of deep neural networks with combinations of Lp-norm cost functions for 3D medical image super-resolution

2020 ◽  
Vol 27 (3) ◽  
pp. 233-251 ◽  
Author(s):  
Karl Thurnhofer-Hemsi ◽  
Ezequiel López-Rubio ◽  
Núria Roé-Vellvé ◽  
Miguel A. Molina-Cabello
Keyword(s):  
Neural Networks ◽  
Multiobjective Optimization ◽  
Medical Image ◽  
Deep Neural Networks ◽  
Super Resolution ◽  
Cost Functions ◽  
Lp Norm ◽  
Image Super Resolution

Speech recognition using deep neural networks trained with non-uniform frame-level cost functions

2017 ◽  
Author(s):  
Aldonso Becerra ◽  
J. Ismael de la Rosa ◽  
Efren Gonzalez ◽  
A. David Pedroza ◽  
J. Manuel Martinez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Deep Neural Networks ◽  
Cost Functions ◽  
Uniform Frame

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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