We lifted 10 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| lins-lab/rded | canonical | 1 of 6 |
| shaoshitong/EDC | reimplementation | 2 of 3 |
| copy not recorded | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| accuracy | Ran | lins-lab/rded/validation/utils.py code served (permissive licence) · get_code("eab4f74c38cc3c8f") |
| gkern | Ran | shaoshitong/EDC/Branch_ImageNet_10/recover/recover.py pointer only (licence: NONE) · get_code("c0e0d0685f8dc4c7") |
| shift_list | Ran | shaoshitong/EDC/Branch_ImageNet_10/recover/recover.py pointer only (licence: NONE) · get_code("871f8dea8a811189") |
| batched_forward | Not yet run | lins-lab/rded/synthesize/utils.py code served (permissive licence) · get_code("b615cefacfc04d10") |
| epoch | Not yet run | shaoshitong/EDC/Branch_CIFAR_10/recover/baseline.py pointer only (licence: NONE) · get_code("cf303e03d7c1efe6") |
| get_parameters | Not yet run | lins-lab/rded/validation/utils.py code served (permissive licence) · get_code("9a700a3b149f4952") |
| get_state_dict | Not yet run | lins-lab/rded/synthesize/utils.py code served (permissive licence) · get_code("b76a08bec5e010fd") |
| keep_top_k | Not yet run | lins-lab/rded/validation/utils.py code served (permissive licence) · get_code("c209acc6c3e5877a") |
| pad | Not yet run | lins-lab/rded/synthesize/utils.py code served (permissive licence) · get_code("680f3862844d8a00") |
| validate | Not yet run | this paper's copy was not recorded; identical code first harvested from VILA-Lab/SRe2L pointer only · get_code("230fb2e9963063c9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Contemporary machine learning requires training large neural networks on massive datasets and thus faces the challenges of high computational demands. Dataset distillation, as a recent emerging strategy, aims to compress real-world datasets for efficient training. However, this line of research currently struggle with large-scale and high-resolution datasets, hindering its practicality and feasibility. To this end, we re-examine the existing dataset distillation methods and identify three properties required for large-scale real-world applications, namely, realism, diversity, and efficiency. As a remedy, we propose RDED, a novel computationally-efficient yet effective data distillation paradigm, to enable both diversity and realism of the distilled data. Extensive empirical results over various neural architectures and datasets demonstrate the advancement of RDED: we can distill the full ImageNet-1K to a small dataset comprising 10 images per class within 7 minutes, achieving a notable 42% top-1 accuracy with ResNet-18 on a single RTX-4090 GPU (while the SOTA only achieves 21% but requires 6 hours).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.03526")
get_code_for_paper("2312.03526")
have("2312.03526")
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