We lifted 8 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| yaolu-zjut/ddinterpreter | canonical | 2 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| distance_wb | Ran | yaolu-zjut/ddinterpreter/methods/DC_DSA_DM/utils.py pointer only (licence: NONE) · get_code("6e9c65aaa7c1b5da") |
| get_dataset_info | Ran | yaolu-zjut/ddinterpreter/utils_clom/Dataloader.py pointer only (licence: NONE) · get_code("a2c08a19541743bb") |
| VGG11 | Not yet run | yaolu-zjut/ddinterpreter/utils_clom/model.py pointer only (licence: NONE) · get_code("8e1b179ef1f0fe03") |
| VGG11BN | Not yet run | yaolu-zjut/ddinterpreter/utils_clom/model.py pointer only (licence: NONE) · get_code("cfab0fafbf988aa4") |
| VGG13 | Not yet run | yaolu-zjut/ddinterpreter/utils_clom/model.py pointer only (licence: NONE) · get_code("07962bf28fe347a0") |
| get_dataset | Not yet run | yaolu-zjut/ddinterpreter/methods/DC_DSA_DM/utils.py pointer only (licence: NONE) · get_code("4ede4e38dc61074f") |
| get_dataset | Not yet run | yaolu-zjut/ddinterpreter/utils_clom/Dataloader.py pointer only (licence: NONE) · get_code("d6e228049010aa8c") |
| get_network | Not yet run | yaolu-zjut/ddinterpreter/utils_clom/model_pool.py pointer only (licence: NONE) · get_code("ef0e8481768a2222") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training. Meanwhile, Pre-trained Models (PTMs) function as knowledge repositories, containing extensive information from the original dataset. This naturally raises a question: Can PTMs effectively transfer knowledge to synthetic datasets, guiding DD accurately? To this end, we conduct preliminary experiments, confirming the contribution of PTMs to DD. Afterwards, we systematically study different options in PTMs, including initialization parameters, model architecture, training epoch and domain knowledge, revealing that: 1) Increasing model diversity enhances the performance of synthetic datasets; 2) Sub-optimal models can also assist in DD and outperform well-trained ones in certain cases; 3) Domain-specific PTMs are not mandatory for DD, but a reasonable domain match is crucial. Finally, by selecting optimal options, we significantly improve the cross-architecture generalization over baseline DD methods. We hope our work will facilitate researchers to develop better DD techniques. Our code is available at https://github.com/yaolu-zjut/DDInterpreter.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.03295")
get_code_for_paper("2310.03295")
have("2310.03295")
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