Hang Zhao, Xinyu Zhang, Jingyong Su, Chenyang Jiang, Zhengcen Li, Qiben Shan, Shaocong Wu
We lifted 8 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| j-cyoung/ADSA_DD | canonical | 1 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| mixup_data | Ran | j-cyoung/ADSA_DD/SRe2L/cifar10/relabel_cifar_adsa.py pointer only (licence: NONE) · get_code("5d6df350b6eefccd") |
| compute_adjustment_auto_1 | Not yet run | j-cyoung/ADSA_DD/SRe2L/cifar10/relabel_cifar_adsa.py pointer only (licence: NONE) · get_code("1a963c22686424d1") |
| cosine_similarity | Not yet run | j-cyoung/ADSA_DD/EDC/cifar10/train/direct_train_adsa.py pointer only (licence: NONE) · get_code("a8532ee9616858c1") |
| get_data_path | Not yet run | j-cyoung/ADSA_DD/perturbation_analysis/compute_entropy.py pointer only (licence: NONE) · get_code("6c170c5c08c5f7d1") |
| get_loader | Not yet run | j-cyoung/ADSA_DD/perturbation_analysis/compute_entropy.py pointer only (licence: NONE) · get_code("92b50eb6e14294c7") |
| inter_class_relation | Not yet run | j-cyoung/ADSA_DD/EDC/cifar10/train/direct_train_adsa.py pointer only (licence: NONE) · get_code("85e46cf488455cd8") |
| pearson_correlation | Not yet run | j-cyoung/ADSA_DD/EDC/cifar10/train/direct_train_adsa.py pointer only (licence: NONE) · get_code("593d10016514986d") |
| validate | Not yet run | j-cyoung/ADSA_DD/GVBSM/cifar10/relabel/generate_soft_label_with_db_adsa.py pointer only (licence: NONE) · get_code("2bce901a6c0d038c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Dataset distillation compresses large-scale datasets into compact, highly informative synthetic data, significantly reducing storage and training costs. However, existing research primarily focuses on balanced datasets and struggles to perform under real-world long-tailed distributions. In this work, we emphasize the critical role of soft labels in long-tailed dataset distillation and uncover the underlying mechanisms contributing to performance degradation. Specifically, we derive an imbalance-aware generalization bound for model trained on distilled dataset. We then identify two primary sources of soft-label bias, which originate from the distillation model and the distilled images, through systematic perturbation of the data imbalance levels. To address this, we propose ADSA, an Adaptive Soft-label Alignment module that calibrates the entangled biases. This lightweight module integrates seamlessly into existing distillation pipelines and consistently improves performance. On ImageNet-1k-LT with EDC and IPC=50, ADSA improves tailclass accuracy by up to 11.8% and raises overall accuracy to 41.4%. Extensive experiments demonstrate that ADSA provides a robust and generalizable solution under limited label budgets and across a range of distillation techniques. Code is available at: https://github.com/j-cyoung/ADSA_DD.git.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2511.17914")
get_code_for_paper("2511.17914")
have("2511.17914")
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