Efficient Augmentation via Data Subsampling
Authors: Michael Kuchnik, Virginia Smith
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments throughout on common benchmark datasets, such as MNIST (Le Cun et al., 1998), CIFAR10 (Krizhevsky, 2009), and NORB (Le Cun et al., 2004). |
| Researcher Affiliation | Academia | Michael Kuchnik & Virginia Smith Carnegie Mellon University {mkuchnik,smithv}@cmu.edu |
| Pseudocode | No | The paper describes its methods in prose and mathematical equations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available online2. 2https://github.com/mkuchnik/Efficient_Augmentation |
| Open Datasets | Yes | We perform experiments throughout on common benchmark datasets, such as MNIST (Le Cun et al., 1998), CIFAR10 (Krizhevsky, 2009), and NORB (Le Cun et al., 2004). |
| Dataset Splits | No | The paper specifies training and test class splits for datasets (e.g., 'The MNIST train class split is 517/483, and its test class split is 1010/974.') but does not explicitly define a separate validation dataset split or a general cross-validation setup for the main experiment evaluation. |
| Hardware Specification | Yes | The system which was used for the test has an Intel i7-6700k and an Nvidia GTX 1080 using CUDA 9.2 and Cu DNN 7.2.1. |
| Software Dependencies | Yes | Tensorflow (Abadi et al., 2015) version 1.10.1 with a variable number of training examples obtained from CIFAR10. The system which was used for the test has an Intel i7-6700k and an Nvidia GTX 1080 using CUDA 9.2 and Cu DNN 7.2.1. |
| Experiment Setup | Yes | Both Le Net and the Keras neural network were fast to train, so we retrained the models for 40 50 epochs with Adam (Kingma & Ba, 2014) and a minibatch size of 512, which was enough to obtain convergence. |