Methods › General › Knowledge Distillation › Teacher-Tutor-Student Knowledge Distillation
Teacher-Tutor-Student Knowledge Distillation
Introduced by Yuying Ge et al. in Parser-Free Virtual Try-on via Distilling Appearance Flows
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Teacher-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.
Papers archive 2025-07-28
4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging 14 Dec 2021 · 1 repository · arXiv:2112.07252
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Distilling Audio-Visual Knowledge by Compositional Contrastive Learning 22 Apr 2021 · 1 repository · arXiv:2104.10955Syntology ran 0 of 1 samples · 1 unverified
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Parser-Free Virtual Try-on via Distilling Appearance Flows 8 Mar 2021 · 2 repositories · arXiv:2103.04559
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Self-supervised driven consistency training for annotation efficient histopathology image analysis 7 Feb 2021 · 2 repositories · arXiv:2102.03897
Tasks archive 2025-07-28
19 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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