Author: Thierry BOUWMANS, Associate Professor, Lab. MIA, Univ. Rochelle, France.
Further Improvements
If you would like to list your publication related to this topic on this website, please send me your publication in .pdf and I will add the reference.
Fair Use Policy
As this website gives many information that come from my research, please cite my following survey papers:
T. Bouwmans, S. Javed, M. Sultana, S. Jung, “Deep Neural Network Concepts in Background Subtraction: A Systematic Review and A Comparative Evaluation”, Neural Networks, Volume 117, pages 7-66, September 2019.
J. Giraldo, H. Le, T. Bouwmans, "Deep Learning based Background Subtraction: A Systematic Survey", 6th Handbook of "Pattern Recognition and Computer Vision", Edited by C.H Chen, World Scientific Publishing, March 2020.
Objective
The aim of this web site is to provide ressources such as references (117 papers), codes and links to demonstration websites for the research on deep neural networks by grouping all related researches and particularly recent advances in this field. For this, it is organized in the following sections:
A) Story Aspects
B) Differents DNNs (13 papers)
Restricted Boltzman (1 paper), Deep Belief Networks (2 papers), AutoEncoders (AEs) (0 paper), Deep Convolutional Networks (2 papers), Deep Probabilistic Neural Networks (1 paper), Deep Fuzzy Neural Networks (2 papers), Spiking Neural Networks (3 papers), Generative Adversarial Neural Networks (2 papers).
C) Theoretical Aspects (44 papers)
Architectures (5 papers), Optimization Algorithms (3 papers), Generalization/Regularization Techniques (3 papers),
Stability/Robustness (6 papers), Interpretability/ Understanding (3 papers), Provability/Stability (3 papers),
Adversarial Perturbations (19 papers), Noisy Labels (2 papers)
D) Implementation Aspects
E) Applications in Computer Vision (31 papers)
F) Surveys (29 papers)