Dataset Condensation with Gradient Matching
Authors: Bo Zhao, Konda Reddy Mopuri, Hakan Bilen
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | 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 {bo.zhao, kmopuri, hbilen}@ed.ac.uk |
| 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. |