Knowledge Distillation
Teacher-Tutor-Student Knowledge Distillation
Introduced by Ge et al. in Parser-Free Virtual Try-on via Distilling Appearance FlowsTeacher-Tutor-Student Knowledge Distillation is a method for image virtual try-on models. It treats fake images produced by the parser-based method as "tutor knowledge", where the artifacts can be corrected by real "teacher knowledge", which is extracted from the real person images in a self-supervised way. Other than using real images as supervisions, knowledge distillation is formulated in the try-on problem as distilling the appearance flows between the person image and the garment image, enabling the finding of dense correspondences between them to produce high-quality results.
Source: Parser-Free Virtual Try-on via Distilling Appearance Flows
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| ECG based Sleep Staging | 1 | 6.67% |
| EEG | 1 | 6.67% |
| EEG based sleep staging | 1 | 6.67% |
| Electroencephalogram (EEG) | 1 | 6.67% |
| Sleep Stage Detection | 1 | 6.67% |
| Sleep Staging | 1 | 6.67% |
| W-R-L-D Sleep Staging | 1 | 6.67% |
| W-R-N Sleep Staging | 1 | 6.67% |
| Audio Tagging | 1 | 6.67% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
