scholarly journals Use of superordinate labels yields more robust and human-like visual representations in convolutional neural networks

2021 ◽  
Vol 21 (13) ◽  
pp. 13
Author(s):  
Seoyoung Ahn ◽  
Gregory J. Zelinsky ◽  
Gary Lupyan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Visual Representations

scholarly journals Dependence Representation Learning with Convolutional Neural Networks and 2D Histograms

2020 ◽  
Vol 10 (3) ◽  
pp. 955
Author(s):  
Taejun Kim ◽  
Han-joon Kim
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Visual Representations ◽  
Representation Learning ◽  
Single Image ◽  
Feature Generation ◽  
Average Classification Accuracy ◽  
Scatter Plots ◽  
Better Than

Researchers frequently use visualizations such as scatter plots when trying to understand how random variables are related to each other, because a single image represents numerous pieces of information. Dependency measures have been widely used to automatically detect dependencies, but these measures only take into account a few types of data, such as the strength and direction of the dependency. Based on advances in the applications of deep learning to vision, we believe that convolutional neural networks (CNNs) can come to understand dependencies by analyzing visualizations, as humans do. In this paper, we propose a method that uses CNNs to extract dependency representations from 2D histograms. We carried out three sorts of experiments and found that CNNs can learn from visual representations. In the first experiment, we used a synthetic dataset to show that CNNs can perfectly classify eight types of dependency. Then, we showed that CNNs can predict correlations based on 2D histograms of real datasets and visualize the learned dependency representation space. Finally, we applied our method and demonstrated that it performs better than the AutoLearn feature generation algorithm in terms of average classification accuracy, while generating half as many features.

scholarly journals Limits to visual representational correspondence between convolutional neural networks and the human brain

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yaoda Xu ◽  
Maryam Vaziri-Pashkam
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Convolutional Neural Networks ◽  
Real World ◽  
Visual Information ◽  
Visual Representations ◽  
Human Vision ◽  
Brain Responses ◽  
General Correspondence ◽  
The Brain

AbstractConvolutional neural networks (CNNs) are increasingly used to model human vision due to their high object categorization capabilities and general correspondence with human brain responses. Here we evaluate the performance of 14 different CNNs compared with human fMRI responses to natural and artificial images using representational similarity analysis. Despite the presence of some CNN-brain correspondence and CNNs’ impressive ability to fully capture lower level visual representation of real-world objects, we show that CNNs do not fully capture higher level visual representations of real-world objects, nor those of artificial objects, either at lower or higher levels of visual representations. The latter is particularly critical, as the processing of both real-world and artificial visual stimuli engages the same neural circuits. We report similar results regardless of differences in CNN architecture, training, or the presence of recurrent processing. This indicates some fundamental differences exist in how the brain and CNNs represent visual information.

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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