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Deep Learning with Generative Models for Anomaly
Il y a 3 mois
Deep Learning with Generative Models for Anomaly Detection:
- Réf
ABG-124048 - Sujet de Thèse 22/05/2024
- Autre financement public
- LITIS Lab, Université de Rouen Normandie
- Lieu de travail
- Rouen
- Normandie
- France
- Intitulé du sujet
- Deep Learning with Generative Models for Anomaly Detection
- Champs scientifiques
- Informatique
- Numérique
- Science de la donnée (stockage, sécurité, mesure, analyse)
Mots clés
- Deep learning, generative (probabilistic) models, diffusion probabilistic models, normalizing flows, anomaly detection, time series
Description du sujet:
PhD Thesis Description
The broad interest in deep neural networks has driven recent advances in anomaly detection, also called out-of-distribution or novelty detection.
Deep one-class, variational autoencoders and generative adversarial networks [1, 2].
While these methods do not allow an exact evaluation of the probability density of new samples, they also suffer from notorious training instability (mode collapse, posterior collapse, vanishing gradients and non-convergence), as corroborated by many research studies [3].
For these reasons, we aim to go beyond the weaknesses of these methods, by investigating novel classes of generative models with deep learning to address anomaly detection.
Research Environment
References
[1] L. Ruff et al., "A unifying review of deep and shallow anomaly detection," Proceedings of the IEEE, 2021.
[2] G. Pang et al.
, "Deep learning for anomaly detection:
A review," ACM Computing Surveys, 2021.
[3] D. Saxena and J. Cao, "Generative adversarial networks (GANs) challenges, solutions, and future directions," ACM Computing Surveys, 2021.
[4] I. Kobyzev et al.
, "Normalizing flows:
An introduction and review of Current Methods," IEEE T PAMI, 2021.
[5] G. Papamakarios et al., "Normalizing flows for probabilistic modeling and inference," JMLR, 2021.
[6] J. Ho et al., "Denoising diffusion probabilistic models," NeurIPS, 2020.
[8] F.A. Croitoru et al.
, "Diffusion models in vision:
A survey," IEEE T PAMI, 2023.
[9] A. Kazerouni et al.
, "Diffusion models in medical imaging:
A comprehensive survey," Medical Image Analysis, 2023.
[10] J. Wolleb et al., "Diffusion models for medical anomaly detection." MICCAI, 2022.
[11] W.H. Pinaya et al., "Fast unsupervised brain anomaly detection and segmentation with diffusion models," MICCAI, 2022.
[12] A. Kascenas et al., "The role of noise in denoising models for anomaly detection in medical images." Medical Image Analysis, 2023.
[13] Y
Chen et al, "ImDiffusion:
Imputed Diffusion Models for Multivariate Time Series Anomaly Detection," in Proc. of the VLDB Endowment, 2023.
[14] R. Hu et al., "Unsupervised Anomaly Detection for Multivariate Time Series Using Diffusion Model," IEEE ICASSP, 2024.
[15] P. Kirichenko et al., "Why normalizing flows fail to detect out-of-distribution data," NeurIPS, 2020.
Nature du financement:
- Autre financement public
Précisions sur le financement:
- ANR (financement acquis)
Présentation établissement et labo d'accueil: - LITIS Lab, Université de Rouen Normandie
Université de Rouen Normandie, INSA Rouen Normandie et Université du Havre Normandie
Site web:
Intitulé du doctorat:
- Doctorat en informatique
Pays d'obtention du doctorat: - France
Etablissement délivrant le doctorat:
- UNIVERSITE DE ROUEN
Ecole doctorale: - Mathématiques, Information, Ingénierie des Systèmes (MIIS)
- Master or Engineering degree, in data science, AI, applied mathematics, or related fields.
- Strong skills in advanced statistics and Machine Learning, including Deep Learning
- Good programming experience in Python 22/06/2024
