scholarly journals Deep Learning Face Attributes in the Wild

2015 ◽  
Author(s):  
Ziwei Liu ◽  
Ping Luo ◽  
Xiaogang Wang ◽  
Xiaoou Tang
Keyword(s):  
Deep Learning ◽  
In The Wild

scholarly journals The Affective Computing Approach to Affect Measurement

2017 ◽  
Vol 10 (2) ◽  
pp. 174-183 ◽  
Author(s):  
Sidney D’Mello ◽  
Arvid Kappas ◽  
Jonathan Gratch
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Affective Computing ◽  
Measurement Accuracy ◽  
Computational Approach ◽  
Tipping Points ◽  
Affective Science ◽  
In The Wild ◽  
Machine Readable ◽  
Computing Approach

Affective computing (AC) adopts a computational approach to study affect. We highlight the AC approach towards automated affect measures that jointly model machine-readable physiological/behavioral signals with affect estimates as reported by humans or experimentally elicited. We describe the conceptual and computational foundations of the approach followed by two case studies: one on discrimination between genuine and faked expressions of pain in the lab, and the second on measuring nonbasic affect in the wild. We discuss applications of the measures, analyze measurement accuracy and generalizability, and highlight advances afforded by computational tipping points, such as big data, wearable sensing, crowdsourcing, and deep learning. We conclude by advocating for increasing synergies between AC and affective science and offer suggestions toward that direction.

scholarly journals A Comparison of Optimization Algorithms for Deep Learning

2020 ◽  
Vol 34 (13) ◽  
pp. 2052013 ◽  
Author(s):  
Derya Soydaner
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Optimization Algorithms ◽  
Gradient Methods ◽  
The Past ◽  
Basic Optimization ◽  
In The Wild ◽  
Image Datasets

In recent years, we have witnessed the rise of deep learning. Deep neural networks have proved their success in many areas. However, the optimization of these networks has become more difficult as neural networks going deeper and datasets becoming bigger. Therefore, more advanced optimization algorithms have been proposed over the past years. In this study, widely used optimization algorithms for deep learning are examined in detail. To this end, these algorithms called adaptive gradient methods are implemented for both supervised and unsupervised tasks. The behavior of the algorithms during training and results on four image datasets, namely, MNIST, CIFAR-10, Kaggle Flowers and Labeled Faces in the Wild are compared by pointing out their differences against basic optimization algorithms.

scholarly journals A Review of Facial Landmark Extraction in 2D Images and Videos Using Deep Learning

2019 ◽  
Vol 3 (1) ◽  
pp. 14 ◽  
Author(s):  
Matteo Bodini
Keyword(s):  
Deep Learning ◽  
Future Research ◽  
3D Face Reconstruction ◽  
Research Directions ◽  
Facial Expression Analysis ◽  
Facial Landmark ◽  
Learning Techniques ◽  
In The Wild ◽  
Future Research Directions ◽  
2D Images

The task of facial landmark extraction is fundamental in several applications which involve facial analysis, such as facial expression analysis, identity and face recognition, facial animation, and 3D face reconstruction. Taking into account the most recent advances resulting from deep-learning techniques, the performance of methods for facial landmark extraction have been substantially improved, even on in-the-wild datasets. Thus, this article presents an updated survey on facial landmark extraction on 2D images and video, focusing on methods that make use of deep-learning techniques. An analysis of many approaches comparing the performances is provided. In summary, an analysis of common datasets, challenges, and future research directions are provided.

Efficient Hybrid Descriptor for Face Verification in the Wild Using the Deep Learning Approach

2019 ◽  
Vol 28 (3) ◽  
pp. 151-164
Author(s):  
Bilel Ameur ◽  
Mebarka Belahcene ◽  
Sabeur Masmoudi ◽  
Ahmed Ben Hamida
Keyword(s):  
Deep Learning ◽  
Face Verification ◽  
Learning Approach ◽  
In The Wild
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