Selecting Data Augmentation for Simulating Interventions
Authors: Maximilian Ilse, Jakub M Tomczak, Patrick Forré
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiments We evaluate the performance of data augmentation in combination with Empirical Risk Minimization (ERM) (Vapnik, 1992) on four datasets. While the first is a synthetic dataset, the other three are domain generalization benchmark image datasets (rotated MNIST, colored MNIST, and PACS) where the domain d and target y are confounded. |
| Researcher Affiliation | Academia | 1Amsterdam Machine Learning Lab, University of Amsterdam 2Computational Intelligence Group, Vrije Universiteit Amsterdam. |
| Pseudocode | No | The paper describes the proposed SDA algorithm in a numbered list using prose, but it does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Code to replicate all experiments can be found under https://github.com/AMLab-Amsterdam/ Data Augmentation Interventions. |
| Open Datasets | Yes | We evaluate the performance of data augmentation in combination with Empirical Risk Minimization (ERM) (Vapnik, 1992) on four datasets. While the first is a synthetic dataset, the other three are domain generalization benchmark image datasets (rotated MNIST, colored MNIST, and PACS) where the domain d and target y are confounded. The PACS dataset (Li et al., 2017a) was introduced as a strong benchmark dataset for domain generalization methods... |
| Dataset Splits | Yes | 1. We divide all samples from the training domains into a training and validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions machine learning models and general frameworks but does not provide specific version numbers for any software libraries, dependencies, or programming languages used (e.g., 'PyTorch 1.9'). |
| Experiment Setup | Yes | The hyperparameter for each augmentation can be found in the Appendix. In addition, we perform an ablation study showing that SDA reliably picks the most suitable hyperparameters, the results can be found in Table 4 in the Appendix. |