RandAugment: Practical Automated Data Augmentation with a Reduced Search Space
Authors: Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, Quoc Le
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Despite the simplifications, our method achieves state-of-the-art performance on CIFAR-10, SVHN, and Image Net. On Efficient Net-B7, we achieve 84.7% accuracy, a 1.0% increase over baseline augmentation and a 0.4% improvement over Auto Augment on the Image Net dataset. On object detection, the same method used for classification leads to 1.0-1.3% improvement over the baseline augmentation method on COCO. |
| Researcher Affiliation | Industry | Ekin D. Cubuk , Barret Zoph , Jonathon Shlens, Quoc V. Le Google Research, Brain Team {cubuk,barretzoph,shlens,qvl}@google.com |
| Pseudocode | Yes | Figure 3: Python code for Rand Augment based on Num Py. |
| Open Source Code | Yes | Code is available online. |
| Open Datasets | Yes | CIFAR-10 [14], SVHN [24], and Image Net [4] classification tasks. |
| Dataset Splits | Yes | N and M were selected based on the validation performance on 5K held out examples from the training set for 1 and 5 settings for N and M, respectively. |
| Hardware Specification | No | The paper mentions 'GPU hours' for Auto Augment's search, implying the use of GPUs, but does not provide specific models or other hardware details for their own experiments. |
| Software Dependencies | No | The paper mentions 'Num Py' and implies 'TensorFlow' through GitHub links, but no specific version numbers for any software dependencies are provided. |
| Experiment Setup | Yes | The Wide-Res Net models are all trained with K=14 data augmentations over a range of distortion magnitudes M parameterized on a uniform linear scale ranging from [0, 30]. Models are trained for 200 epochs on 45K training set examples. N and M were selected based on the validation performance on 5K held out examples from the training set for 1 and 5 settings for N and M, respectively. |