Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment
Authors: JIAWEI DU, xin zhang, Juncheng Hu, Wenxin Huang, Joey Tianyi Zhou
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across multiple datasets, including CIFAR, Tiny-Image Net, and Image Net-1K, demonstrate the superior performance of our method, highlighting its effectiveness in producing diverse and representative synthetic datasets with minimal computational expense. |
| Researcher Affiliation | Collaboration | 1 Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR), Singapore 2 Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore 3Xi Dian University, Xi an, China 4National University of Singapore, Singapore 5Hubei University, Wu Han, China |
| Pseudocode | Yes | The pseudocode of our proposed DWA can be found in Algorithm 1. |
| Open Source Code | Yes | Our code is available at https://github.com/Angus Dujw/Diversity-Driven-Synthesis. |
| Open Datasets | Yes | To evaluate the effectiveness of the proposed method, we have conducted extensive comparison experiments with SOTA methods on various datasets including CIFAR-10/100 (32 32, 10/100 classes) [16], Tiny-Image Net (64 64, 200 classes) [18], and Image Net-1K (224 224, 1000 classes) [3] |
| Dataset Splits | No | The paper mentions evaluating on the "original validation set" but does not explicitly provide the specific percentages or counts for training, validation, and test splits (e.g., "80/10/10 split") for the datasets used to reproduce the data partitioning. |
| Hardware Specification | Yes | We conduct our experiments on the server with one Nvidia Tesla A100 40GB GPU. |
| Software Dependencies | No | Optimizer Adam with {β1, β2} = {0.5, 0.9} Optimizer Adam W with weight decay of 0.01. The paper mentions optimizers but does not list specific versions for software libraries like PyTorch, TensorFlow, or Python itself. |
| Experiment Setup | Yes | More detailed hyper-parameter settings can be found in Appendix A.2.1. |