Add to ORKGNeural Networks are prone to having lesser accuracy in the classification of images with noise perturbation. Convolutional Neural Networks, CNNs are known for their unparalleled accuracy in the classification of benign images. But our study shows that they are extremely vulnerable to noise addition while Feed-forward Neural Networks, FNNs show very less correspondence with noise perturbation, maintaining their accuracy almost undisturbed. FNNs are observed to be better at classifying noise-intensive, single-channeled images that are just sheer noise to human vision. In our study, we have used the hand-written digits dataset, MNIST with the following architectures: FNNs with 1 and 2 hidden layers and CNNs with 3, 4, 6 and 8 convolutions and ...
Recent discoveries uncovered flaws in machine learning algorithms such as deep neural networks. Deep...
Recent experiments in computer vision demonstrate texture bias as the primary reason for supreme res...
First we present a proof that convolutional neural networks (CNNs) with max-norm regularization, max...
Deep Learning (read neural networks) has emerged as one of the most exciting and powerful tools in t...
Convolutional Neural Networks (CNN) are extremely popular for modelling sound and images, but they s...
Abstract: Background: From Previous research, state-of-the-art deep neural networks have accomplishe...
Presented online via Bluejeans Meetings on November 29, 2021 at 11:15 a.m.Frank Tong is the Centenni...
It is widely acknowledged that trained convolutional neural networks (CNNs) have different levels of...
Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a res...
It is of significant importance for any classification and recognition system, which claims near or ...
Deep Neural Networks (DNNs) are widely used for decision making in a myriad of critical applications...
Some recent artificial neural networks (ANNs) have claimed to model important aspects of primate neu...
Modern artificial neural networks, including convolutional neural networks and vision transformers, ...
Today the amount of applications which use Neural Networks is increasing every day. The scope of us...
Despite superior accuracy on most vision recognition tasks, deep neural networks are susceptible to ...
Recent discoveries uncovered flaws in machine learning algorithms such as deep neural networks. Deep...
Recent experiments in computer vision demonstrate texture bias as the primary reason for supreme res...
First we present a proof that convolutional neural networks (CNNs) with max-norm regularization, max...
Deep Learning (read neural networks) has emerged as one of the most exciting and powerful tools in t...
Convolutional Neural Networks (CNN) are extremely popular for modelling sound and images, but they s...
Abstract: Background: From Previous research, state-of-the-art deep neural networks have accomplishe...
Presented online via Bluejeans Meetings on November 29, 2021 at 11:15 a.m.Frank Tong is the Centenni...
It is widely acknowledged that trained convolutional neural networks (CNNs) have different levels of...
Deep learning has outperformed other machine learning algorithms in a variety of tasks, and as a res...
It is of significant importance for any classification and recognition system, which claims near or ...
Deep Neural Networks (DNNs) are widely used for decision making in a myriad of critical applications...
Some recent artificial neural networks (ANNs) have claimed to model important aspects of primate neu...
Modern artificial neural networks, including convolutional neural networks and vision transformers, ...
Today the amount of applications which use Neural Networks is increasing every day. The scope of us...
Despite superior accuracy on most vision recognition tasks, deep neural networks are susceptible to ...
Recent discoveries uncovered flaws in machine learning algorithms such as deep neural networks. Deep...
Recent experiments in computer vision demonstrate texture bias as the primary reason for supreme res...
First we present a proof that convolutional neural networks (CNNs) with max-norm regularization, max...