Papers › Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification
Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification
Paul-Dumitru Orasan, Alexandra-Ioana Albu, Gabriela Czibula
Diffusion models have revolutionized the field of generative machine learning due to their effectiveness in capturing complex, multimodal data distributions. Semi-supervised learning represents a technique that allows the extraction of information from a large corpus of unlabeled data, assuming that a small subset of labeled data is provided. While many generative methods have been previously used in semi-supervised learning tasks, only few approaches have integrated diffusion models in such a context. In this work, we are adapting state-of-the-art generative diffusion models to the problem of semi-supervised image classification. We propose Diff-SySC, a new semi supervised, pseudo-labeling pipeline which uses a diffusion model to learn the conditional probability distribution characterizing the label generation process. Experimental evaluations highlight the robustness of Diff-SySC when evaluated on image classification benchmarks and show that it outperforms related work approaches on CIFAR-10 and STL-10, while achieving competitive performance on CIFAR-100. Overall, our proposed method outperforms the related work in 90.74% of the cases.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Image Classification | CIFAR-10, 250 Labels | Diff-SySC | Percentage error | 3.65±0.10 | #4 of 27 | Archive leaderboard | report |
| Semi-Supervised Image Classification | CIFAR-10, 4000 Labels | Diff-SySC | Percentage error | 3.26±0.06 | #3 of 49 | Archive leaderboard | report |
| Semi-Supervised Image Classification | STL-10, 1000 Labels | Diff-SySC | Accuracy | 99.36±0.20 | #1 of 13 | 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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