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..
Color-Oriented Redundancy Reduction in Dataset Distillation
Authors: Bowen Yuan, Zijian Wang, Mahsa Baktashmotlagh, Yadan Luo, Zi Huang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A comprehensive performance study involving various datasets and evaluation scenarios is conducted, demonstrating the superior performance of our proposed color-aware DD compared to existing DD methods. |
| Researcher Affiliation | Academia | Bowen Yuan Zijian Wang Mahsa Baktashmotlagh Yadan Luo Zi Huang EMAIL The University of Queensland |
| Pseudocode | Yes | Algorithm 1: Algorithm for guided image selection with maximum information gain. |
| Open Source Code | Yes | The code is available at https://github.com/Ke Vi NYu An0314/Auto Palette. |
| Open Datasets | Yes | We conduct experiments of our model on various benchmark datasets, including CIFAR-10 [21], CIFAR-100 [21] and Image Net [7]. CIFAR10: an image dataset consists of 50,000 32 32 RGB images for training, and 10,000 images for testing. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits, only mentioning training and testing sets for CIFAR10 and total image counts for CIFAR100. |
| Hardware Specification | Yes | All experiments can be conducted on 2 Nvidia H100 GPUs that have 80GB RAM for each or 4 Nvidia V100 GPUs that have 32GB RAM for each. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as libraries, frameworks, or compilers used in the experiments. |
| Experiment Setup | Yes | We set loss coefficients α=1, β=1, γ=3 for all experiments if not specified. Table 9: Hyperparameters for our method based on Distribution matching (DM) framework. Table 10: Hyperparameters for our method based on Trajectory matching (TM) framework. These tables provide specific hyperparameters. |