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..
Dataset Condensation with Gradient Matching
Authors: Bo Zhao, Konda Reddy Mopuri, Hakan Bilen
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We rigorously evaluate its performance in several computer vision benchmarks and demonstrate that it significantly outperforms the state-of-the-art methods1. |
| Researcher Affiliation | Academia | Bo Zhao, Konda Reddy Mopuri, Hakan Bilen School of Informatics, The University of Edinburgh EMAIL |
| Pseudocode | Yes | Algorithm 1: Dataset condensation with gradient matching Input: Training set T |
| Open Source Code | Yes | 1The implementation is available at https://github.com/VICO-Uo E/Dataset Condensation. |
| Open Datasets | Yes | We evaluate classification performance with the condensed images on four standard benchmark datasets: digit recognition on MNIST (Le Cun et al., 1998), SVHN (Netzer et al., 2011) and object classification on Fashion MNIST (Xiao et al., 2017), CIFAR10 (Krizhevsky et al., 2009). |
| Dataset Splits | Yes | In all experiments, we use the standard train/test splits of the datasets the train/test statistics are shown in Table T5. We randomly sample 5,000 images from the 50,000 training images in CIFAR10 as the validation set. |
| Hardware Specification | Yes | We compare the training time and memory cost required by DD and our method with one NVIDIA GTX1080-Ti GPU. |
| Software Dependencies | No | The paper mentions using Stochastic Gradient Descent (SGD) as an optimizer but does not specify any software libraries or frameworks with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | In all experiments, we set K = 1000, ηS = 0.1, ηθ = 0.01, ςS = 1 and employ Stochastic Gradient Descent (SGD) as the optimizer. |