Non-uniqueness phenomenon of object representation in modelling IT cortex by deep convolutional neural network (DCNN)

06/06/2019
by   Qiulei Dong, et al.
0

Recently DCNN (Deep Convolutional Neural Network) has been advocated as a general and promising modelling approach for neural object representation in primate inferotemporal cortex. In this work, we show that some inherent non-uniqueness problem exists in the DCNN-based modelling of image object representations. This non-uniqueness phenomenon reveals to some extent the theoretical limitation of this general modelling approach, and invites due attention to be taken in practice.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/06/2018

A Non-Technical Survey on Deep Convolutional Neural Network Architectures

Artificial neural networks have recently shown great results in many dis...
research
12/09/2017

Uniqueness of Transformation based on Jacobian Determinant and curl-Vector

Numerical examples demonstrated that a prescribed positive Jacobian dete...
research
09/08/2015

DeepCough: A Deep Convolutional Neural Network in A Wearable Cough Detection System

In this paper, we present a system that employs a wearable acoustic sens...
research
02/27/2015

Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification

We propose a local modelling approach using deep convolutional neural ne...
research
01/12/2021

UCNN: A Convolutional Strategy on Unstructured Mesh

In machine learning for fluid mechanics, fully-connected neural network ...
research
02/20/2020

Learning Intermediate Features of Object Affordances with a Convolutional Neural Network

Our ability to interact with the world around us relies on being able to...
research
10/23/2013

Can Facial Uniqueness be Inferred from Impostor Scores?

In Biometrics, facial uniqueness is commonly inferred from impostor simi...

Please sign up or login with your details

Forgot password? Click here to reset