Data Augmentation as Feature Manipulation
Authors: Ruoqi Shen, Sebastien Bubeck, Suriya Gunasekar
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our main contribution is a detailed analysis of data augmentation on the learning dynamic for a two layer convolutional neural network in the recently proposed multi-view data model by Allen-Zhu & Li (2020b). We complement this analysis with further experimental evidence that data augmentation can be viewed as feature manipulation. |
| Researcher Affiliation | Collaboration | 1University of Washington. Part of this work was done as a intern at Microsoft Research. 2Microsoft Research. |
| Pseudocode | No | The paper describes algorithms and derivations using mathematical formulas and prose, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We complement our analysis with experiments on CIFAR-10 and synthetic datasets, where we study data augmentation in more generality. |
| Dataset Splits | No | The paper mentions 'training examples' and 'test dataset' (e.g., 'full CIFAR-10 dataset which has 50000 training examples for 10 classes' and 'We use the standard CIFAR-10 test dataset') but does not specify exact training/validation/test split percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions training networks (ResNet20) and using SGD, but it does not specify any software dependencies with version numbers (e.g., PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | In all configurations, we train a Res Net20 network using SGD for 120 epochs with momentum 0.9, weight decay 0.005, and learning rate starting at 0.1 and annealed to (0.01, 0.001) at epochs (40, 80). |