Tradeoffs in Data Augmentation: An Empirical Study
Authors: Raphael Gontijo-Lopes, Sylvia Smullin, Ekin Dogus Cubuk, Ethan Dyer
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Inspired by these, we conduct an empirical study to quantify how data augmentation improves model generalization. We present an empirical study of 204 different augmentations on CIFAR-10 and 225 on Image Net, varying both broad transform families and finer transform parameters. |
| Researcher Affiliation | Industry | Raphael Gontijo-Lopes Google Brain iraphael@google.com Sylvia J. Smullin Blueshift, Alphabet Ekin D. Cubuk Google Brain cubuk@google.com Ethan Dyer Blueshift, Alphabet edyer@google.com |
| Pseudocode | No | The paper describes methods in prose and with mathematical definitions, but does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states it used code based on an existing open-source project ('Cifar10 models were trained using code based on Auto Augment code2... 2available at github.com/tensorflow/models/tree/master/research/autoaugment'), but it does not explicitly state that the specific code for their described methodology (e.g., the Affinity and Diversity metrics' implementation or their experimental scripts) is open-source or provided. |
| Open Datasets | Yes | We present an empirical study of 204 different augmentations on CIFAR-10 and 225 on Image Net... |
| Dataset Splits | Yes | Validation set was the last 5000 samples of the shuffled CIFAR-10 training data. |
| Hardware Specification | No | The paper states 'Image Net models were Res Net-50 trained using the Cloud TPU codebase' but does not specify the exact model or version of the Cloud TPU (e.g., TPU v2, v3) or other hardware specifications for the experiments. |
| Software Dependencies | Yes | Models were trained using Python 2.7 and Tensor Flow 1.13 . |
| Experiment Setup | Yes | Experiments on CIFAR-10 used the WRN-28-2 model (Zagoruyko & Komodakis, 2016), trained for 78k steps with cosine learning rate decay. ... Experiments on Image Net used the Res Net-50 model (He et al., 2016), trained for 112.6k steps with a weight decay rate of 1e-4, and a learning rate of 0.2, which is decayed by 10 at epochs 30, 60, and 80. Batch size was set to be 1024. |