Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Frequency Domain-Based Dataset Distillation
Authors: Donghyeok Shin, Seungjae Shin, Il-chul Moon
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through the selection of frequency dimensions based on the explained variance, Fre D demonstrates both theoretical and empirical evidence of its ability to operate efficiently within a limited budget, while better preserving the information of the original dataset compared to conventional parameterization methods. Furthermore, based on the orthogonal compatibility of Fre D with existing methods, we confirm that Fre D consistently improves the performances of existing distillation methods over the evaluation scenarios with different benchmark datasets. ... We evaluate the efficacy of Fre D on various benchmark datasets, i.e. SVHN [13], CIFAR-10, CIFAR-100 [8] and Image Net-Subset [6, 3, 4]. |
| Researcher Affiliation | Collaboration | Donghyeok Shin KAIST EMAIL Seungjae Shin KAIST EMAIL Il-Chul Moon KAIST, Summary.AI EMAIL |
| Pseudocode | Yes | Algorithm 1 Fre D: Frequency domain-based Dataset Distillation |
| Open Source Code | Yes | We release the code at https://github.com/sdh0818/Fre D. |
| Open Datasets | Yes | We evaluate the efficacy of Fre D on various benchmark datasets, i.e. SVHN [13], CIFAR-10, CIFAR-100 [8] and Image Net-Subset [6, 3, 4]. |
| Dataset Splits | No | The paper mentions training and evaluation but does not explicitly detail training/validation/test splits (e.g., specific percentages, sample counts, or explicit mention of a validation set beyond general deep learning context). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9, and CUDA 11.1'). |
| Experiment Setup | Yes | We evaluate each method by training 5 randomly initialized networks from scratch on optimized S. Please refer to Appendix C for a detailed explanation of datasets and experiment settings. |