Discriminative feature-space transforms using deep neural networks

2012 ◽  
Author(s):  
George Saon ◽  
Brian Kingsbury
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Feature Space ◽  
Discriminative Feature

scholarly journals Inter-Class Angular Loss for Convolutional Neural Networks

2019 ◽  
Vol 33 ◽  
pp. 3894-3901 ◽  
Author(s):  
Le Hui ◽  
Xiang Li ◽  
Chen Gong ◽  
Meng Fang ◽  
Joey Tianyi Zhou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Learning Difficulties ◽  
Feature Space ◽  
Superior Performance ◽  
Strongly Correlated ◽  
Discriminative Ability ◽  
Practical Applications ◽  
Classification Tasks

Convolutional Neural Networks (CNNs) have shown great power in various classification tasks and have achieved remarkable results in practical applications. However, the distinct learning difficulties in discriminating different pairs of classes are largely ignored by the existing networks. For instance, in CIFAR-10 dataset, distinguishing cats from dogs is usually harder than distinguishing horses from ships. By carefully studying the behavior of CNN models in the training process, we observe that the confusion level of two classes is strongly correlated with their angular separability in the feature space. That is, the larger the inter-class angle is, the lower the confusion will be. Based on this observation, we propose a novel loss function dubbed “Inter-Class Angular Loss” (ICAL), which explicitly models the class correlation and can be directly applied to many existing deep networks. By minimizing the proposed ICAL, the networks can effectively discriminate the examples in similar classes by enlarging the angle between their corresponding class vectors. Thorough experimental results on a series of vision and nonvision datasets confirm that ICAL critically improves the discriminative ability of various representative deep neural networks and generates superior performance to the original networks with conventional softmax loss.

scholarly journals Deep Convolutional Nets Learning Classification for Artistic Style Transfer

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
R. Dinesh Kumar ◽  
E. Golden Julie ◽  
Y. Harold Robinson ◽  
S. Vimal ◽  
Gaurav Dhiman ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Feature Space ◽  
Input Image ◽  
Reference Image ◽  
Nonphotorealistic Rendering ◽  
Style Transfer ◽  
Artistic Style ◽  
The Neural Network

Humans have mastered the skill of creativity for many decades. The process of replicating this mechanism is introduced recently by using neural networks which replicate the functioning of human brain, where each unit in the neural network represents a neuron, which transmits the messages from one neuron to other, to perform subconscious tasks. Usually, there are methods to render an input image in the style of famous art works. This issue of generating art is normally called nonphotorealistic rendering. Previous approaches rely on directly manipulating the pixel representation of the image. While using deep neural networks which are constructed using image recognition, this paper carries out implementations in feature space representing the higher levels of the content image. Previously, deep neural networks are used for object recognition and style recognition to categorize the artworks consistent with the creation time. This paper uses Visual Geometry Group (VGG16) neural network to replicate this dormant task performed by humans. Here, the images are input where one is the content image which contains the features you want to retain in the output image and the style reference image which contains patterns or images of famous paintings and the input image which needs to be style and blend them together to produce a new image where the input image is transformed to look like the content image but “sketched” to look like the style image.

Feature space maximum a posteriori linear regression for adaptation of deep neural networks

2014 ◽  
Author(s):  
Zhen Huang ◽  
Jinyu Li ◽  
Sabato Marco Siniscalchi ◽  
I-Fan Chen ◽  
Chao Weng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Deep Neural Networks ◽  
Feature Space ◽  
Maximum A Posteriori ◽  
A Posteriori

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
Sign in / Sign up

Export Citation Format

Share Document