Generative Models
Conditional / Rectified flow matching
Introduced by Lipman et al. in Flow Matching for Generative ModelingConditional Flow Matching (CFM) is a fast way to train continuous normalizing flow (CNF) models. CFM is a simulation-free training objective for continuous normalizing flows that allows conditional generative modelling and speeds up training and inference.
Source: Flow Matching for Generative Modeling
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Generation | 19 | 8.60% |
| Denoising | 16 | 7.24% |
| Language Modelling | 8 | 3.62% |
| Speech Synthesis | 8 | 3.62% |
| Decoder | 7 | 3.17% |
| Diversity | 7 | 3.17% |
| Voice Conversion | 6 | 2.71% |
| Drug Design | 6 | 2.71% |
| Drug Discovery | 6 | 2.71% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
