Papers › Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?
Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?
Zheng Li, YuXuan Li, Penghai Zhao, RenJie Song, Xiang Li, Jian Yang
Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images. Given their huge success, it is still unclear whether synthetic images are applicable for knowledge distillation when real images are unavailable. In this paper, we extensively study whether and how synthetic images produced from state-of-the-art diffusion models can be used for knowledge distillation without access to real images, and obtain three key conclusions: (1) synthetic data from diffusion models can easily lead to state-of-the-art performance among existing synthesis-based distillation methods, (2) low-fidelity synthetic images are better teaching materials, and (3) relatively weak classifiers are better teachers. Code is available at https://github.com/zhengli97/DM-KD.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Learning | DTD | Real-Guidance + CAL | 12-shot Accuracy | 54.5 | #2 of 4 | Archive leaderboard | report |
| Few-Shot Learning | DTD | Real-Guidance + CAL | 16-shot Accuracy | 57.4 | #2 of 4 | Archive leaderboard | report |
| Few-Shot Learning | DTD | Real-Guidance + CAL | 4-shot Accuracy | 41.5 | #2 of 4 | Archive leaderboard | report |
| Few-Shot Learning | DTD | Real-Guidance + CAL | 8-shot Accuracy | 50.6 | #2 of 4 | Archive leaderboard | report |
| Few-Shot Learning | FGVC Aircraft | Real-Guidance + CAL | 12-shot Accuracy | 65.8 | #4 of 4 | Archive leaderboard | report |
| Few-Shot Learning | FGVC Aircraft | Real-Guidance + CAL | 16-shot Accuracy | 72.5 | #4 of 4 | Archive leaderboard | report |
| Few-Shot Learning | FGVC Aircraft | Real-Guidance + CAL | 4-shot Accuracy | 34.5 | #4 of 4 | Archive leaderboard | report |
| Few-Shot Learning | FGVC Aircraft | Real-Guidance + CAL | 8-shot Accuracy | 54.6 | #4 of 4 | Archive leaderboard | report |
| Few-Shot Learning | FGVC Aircraft | Real-Guidance + CAL | Harmonic mean | 34.5 | #4 of 4 | Archive leaderboard | report |
| Few-Shot Learning | Stanford Cars | Real-Guidance + CAL | 12-shot Accuracy | 83.9 | #2 of 3 | Archive leaderboard | report |
| Few-Shot Learning | Stanford Cars | Real-Guidance + CAL | 16-shot Accuracy | 88.3 | #2 of 3 | Archive leaderboard | report |
| Few-Shot Learning | Stanford Cars | Real-Guidance + CAL | 4-shot Accuracy | 44.3 | #2 of 3 | Archive leaderboard | report |
| Few-Shot Learning | Stanford Cars | Real-Guidance + CAL | 8-shot Accuracy | 73.1 | #2 of 3 | Archive leaderboard | report |
| Mitigating Contextual Bias | FGVC Aircraft | CAL + Real-Guidance | OOD Accuracy (%) | 17.7 | #3 of 4 | Archive leaderboard | report |
| Mitigating Contextual Bias | FGVC Aircraft | CAL + Real-Guidance | Top-1 Accuracy (%) | 71.7 | #3 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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