AIMC Topic: Pattern Recognition, Visual
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Principal Component Analysis based on Nuclear norm Minimization.
Neural networks : the official journal of the International Neural Network Society
Jun 8, 2019
Principal component analysis (PCA) is a widely used tool for dimensionality reduction and feature extraction in the field of computer vision. Traditional PCA is sensitive to outliers which are common in empirical applications. Therefore, in recent ye...
Reconstructing faces from fMRI patterns using deep generative neural networks.
Communications biology
May 21, 2019
Although distinct categories are reliably decoded from fMRI brain responses, it has proved more difficult to distinguish visually similar inputs, such as different faces. Here, we apply a recently developed deep learning system to reconstruct face im...
Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex.
NeuroImage
May 16, 2019
Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE)...
An unsupervised neuromorphic clustering algorithm.
Biological cybernetics
Apr 3, 2019
Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, hi...
Emotional arousal amplifies competitions across goal-relevant representation: A neurocomputational framework.
Cognition
Mar 8, 2019
Emotional arousal often facilitates memory for some aspects of an event while impairing memory for other aspects of the same event. Across three experiments, we found that emotional arousal amplifies competition among goal-relevant representations, s...
System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia.
Sensors (Basel, Switzerland)
Jan 30, 2019
Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system...
Features Combined From Hundreds of Midlayers: Hierarchical Networks With Subnetwork Nodes.
IEEE transactions on neural networks and learning systems
Jan 28, 2019
In this paper, we believe that the mixed selectivity of neuron in the top layer encodes distributed information produced from other neurons to offer a significant computational advantage over recognition accuracy. Thus, this paper proposes a hierarch...
Brain-inspired automated visual object discovery and detection.
Proceedings of the National Academy of Sciences of the United States of America
Dec 17, 2018
Despite significant recent progress, machine vision systems lag considerably behind their biological counterparts in performance, scalability, and robustness. A distinctive hallmark of the brain is its ability to automatically discover and model obje...
Deep convolutional networks do not classify based on global object shape.
PLoS computational biology
Dec 7, 2018
Deep convolutional networks (DCNNs) are achieving previously unseen performance in object classification, raising questions about whether DCNNs operate similarly to human vision. In biological vision, shape is arguably the most important cue for reco...
Emergent neural turing machine and its visual navigation.
Neural networks : the official journal of the International Neural Network Society
Nov 27, 2018
Traditional Turing Machines (TMs) are symbolic whose hand-crafted representations are static and limited. Developmental Network 1 (DN-1) uses emergent representation to perform Turing Computation. But DN-1 lacks hierarchy in its internal representati...
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