ABlackx/MDCC: MDCC v1.0
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2025
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| _version_ | 1866901724664430592 |
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| author | ABlackx |
| author_facet | ABlackx |
| contents | <p>MDCC v1.0 — First public code release</p> <p>This release publishes the codebase for our article "<strong>Feature mixing-driven dynamic contrastive consistency learning for robust source-free unsupervised domain adaptation</strong>", which is being submitted to <em>The Visual Computer</em>. We are opening the implementation to support reproducibility during editorial review and for the community.</p> <p><strong>What's included</strong></p> <ul> <li>Core MDCC implementation: Feature Mixing module, dynamic contrastive consistency learning, and a divide-and-conquer training schedule.</li> <li>Training scripts and example configs.</li> <li>Dataset preparation notes for Office-31, Office-Home, VisDA-C, and DomainNet-126.</li> </ul> <p><strong>Quick start</strong></p> <ul> <li>Prepare datasets and run training as described in the README.</li> </ul> <p><strong>Notes</strong></p> <ul> <li>Citation information will be added after submission/decision.</li> </ul> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16795363 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ABlackx/MDCC: MDCC v1.0 ABlackx <p>MDCC v1.0 — First public code release</p> <p>This release publishes the codebase for our article "<strong>Feature mixing-driven dynamic contrastive consistency learning for robust source-free unsupervised domain adaptation</strong>", which is being submitted to <em>The Visual Computer</em>. We are opening the implementation to support reproducibility during editorial review and for the community.</p> <p><strong>What's included</strong></p> <ul> <li>Core MDCC implementation: Feature Mixing module, dynamic contrastive consistency learning, and a divide-and-conquer training schedule.</li> <li>Training scripts and example configs.</li> <li>Dataset preparation notes for Office-31, Office-Home, VisDA-C, and DomainNet-126.</li> </ul> <p><strong>Quick start</strong></p> <ul> <li>Prepare datasets and run training as described in the README.</li> </ul> <p><strong>Notes</strong></p> <ul> <li>Citation information will be added after submission/decision.</li> </ul> |
| title | ABlackx/MDCC: MDCC v1.0 |
| url | https://doi.org/10.5281/zenodo.16795363 |