mlsanityData to 7 Oct 2026

Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal

arXiv 2106.01467 ↗First posted 2 Jun 2021cs.CV cs.LGcited by 0

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Abstract (arXiv)

Bringing empathy to a computerized system could significantly improve the quality of human-computer communications, as soon as machines would be able to understand customer intentions and better serve their needs. According to different studies (Literature Review), visual information is one of the most important channels of human interaction and contains significant behavioral signals, that may be captured from facial expressions. Therefore, it is consistent and natural that the research in the field of Facial Expression Recognition (FER) has acquired increased interest over the past decade due to having diverse application area including health-care, sociology, psychology, driver-safety, virtual reality, cognitive sciences, security, entertainment, marketing, etc. We propose a new architecture for the task of FER and examine the impact of domain discrimination loss regularization on the learning process. With regard to observations, including both classical training conditions and unsupervised domain adaptation scenarios, important aspects of the considered domain adaptation approach integration are traced. The results may serve as a foundation for further research in the field.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 81 references that are in the map. First posted on arXiv 2 Jun 2021.

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33 of 81 references.

References · 33

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2014Going Deeper with Convolutionscited by 4,231
  3. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  4. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,323
  5. 2017Learning to Generalize: Meta-Learning for Domain Generalizationcited by 762
  6. 2017AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wildcited by 490
  7. 2013Do Deep Nets Really Need to be Deep?cited by 466
  8. 2020Self-Challenging Improves Cross-Domain Generalizationcited by 426
  9. 2013Challenges in Representation Learning: A report on three machine learning contestscited by 301
  10. 2018Deep Facial Expression Recognition: A Surveycited by 269
  11. 2015Domain Generalization for Object Recognition with Multi-task Autoencoderscited by 253
  12. 2019Episodic Training for Domain Generalizationcited by 245
21 more
  1. 2015Simultaneous Deep Transfer Across Domains and Taskscited by 205
  2. 2016Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distributioncited by 158
  3. 2018Facial Landmark Detection: a Literature Surveycited by 63
  4. 2016Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applicationscited by 45
  5. 2015Going Deeper in Facial Expression Recognition using Deep Neural Networkscited by 43
  6. 2019Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Networkcited by 37
  7. 2018Exploring Disentangled Feature Representation Beyond Face Identificationcited by 36
  8. 2016FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognitioncited by 25
  9. 2020Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networkscited by 24
  10. 2016Peak-Piloted Deep Network for Facial Expression Recognitioncited by 22
  11. 2015Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?cited by 19
  12. 2014Zero-bias autoencoders and the benefits of co-adapting featurescited by 15
  13. 2019Identity-Free Facial Expression Recognition using conditional Generative Adversarial Networkcited by 14
  14. 2020Coding Facial Expressions with Gabor Wavelets (IVC Special Issue)cited by 11
  15. 2016Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machinescited by 10
  16. 2014An Analysis of Unsupervised Pre-training in Light of Recent Advancescited by 8
  17. 2017Deep generative-contrastive networks for facial expression recognitioncited by 7
  18. 2016Facial expression recognition based on local region specific features and support vector machinescited by 5
  19. 2017MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Imagescited by 4
  20. 2019Facial Expression Recognition Using Disentangled Adversarial Learningcited by 3
  21. 2020Learning Disentangled Expression Representations from Facial Imagescited by 3

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