Conference
Learning Diffusion Priors from Observations by Expectation Maximization
| Τίτλος: | Learning Diffusion Priors from Observations by Expectation Maximization |
|---|---|
| Συγγραφείς: | Rozet, François, Andry, Gérôme, Lanusse, François, Louppe, Gilles |
| Πηγή: | Advances in Neural Information Processing Systems, 37 (2024-05-22); The Thirty-Eighth Annual Conference on Neural Information Processing Systems, Vancouver, Canada [CA], December 10-15, 2024 |
| Στοιχεία εκδότη: | Curran Associates, 2024. |
| Έτος έκδοσης: | 2024 |
| Θεματικοί όροι: | Computer Science - Learning, Statistics - Machine Learning, Engineering, computing & technology, Computer science, Ingénierie, informatique & technologie, Sciences informatiques |
| Περιγραφή: | Diffusion models recently proved to be remarkable priors for Bayesian inverse problems. However, training these models typically requires access to large amounts of clean data, which could prove difficult in some settings. In this work, we present DiEM, a novel method based on the expectation-maximization algorithm for training diffusion models from incomplete and noisy observations only. Unlike previous works, DiEM leads to proper diffusion models, which is crucial for downstream tasks. As part of our methods, we propose and motivate an improved posterior sampling scheme for unconditional diffusion models. We present empirical evidence supporting the effectiveness of our approach. |
| Τύπος εγγράφου: | conference paper http://purl.org/coar/resource_type/c_5794 conferenceObject peer reviewed |
| Γλώσσα: | English |
| Relation: | https://openreview.net/forum?id=7v88Fh6iSM; https://openreview.net/forum?id=7v88Fh6iSM; urn:issn:1049-5258 |
| Σύνδεσμος πρόσβασης: | https://orbi.uliege.be/handle/2268/319891 |
| Rights: | open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
| Αριθμός Καταχώρησης: | edsorb.319891 |
| Βάση Δεδομένων: | ORBi |
| Η περιγραφή δεν είναι διαθέσιμη |