MetaAugment: Sample-Aware Data Augmentation Policy Learning
Authors: Fengwei Zhou, Jiawei Li, Chuanlong Xie, Fei Chen, Lanqing Hong, Rui Sun, Zhenguo Li11097-11105
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Superior performance is achieved on widely-used benchmarks including CIFAR-10/100, Omniglot, and Image Net. Experimental Results In this section, we evaluate Meta Augment for image recognition tasks on CIFAR-10/100 (Krizhevsky and Hinton 2009), Omniglot (Lake et al. 2011), and Image Net (Deng et al. 2009). |
| Researcher Affiliation | Industry | Huawei Noah s Ark Lab {zhoufengwei, li.jiawei, xie.chuanlong, chen.f, honglanqing, sun.rui3, li.zhenguo}@huawei.com |
| Pseudocode | Yes | Algorithm 1 Meta Augment: Sample-Aware Data Augmentation Policy Learning |
| Open Source Code | No | The paper states "For FAA and PBA, we do experiments with their open-source codes." but does not explicitly state that the code for Meta Augment is open-source or provide a link to it. |
| Open Datasets | Yes | Experimental Results In this section, we evaluate Meta Augment for image recognition tasks on CIFAR-10/100 (Krizhevsky and Hinton 2009), Omniglot (Lake et al. 2011), and Image Net (Deng et al. 2009). |
| Dataset Splits | Yes | CIFAR. CIFAR-10 and CIFAR-100 consist of 50,000 images for training and 10,000 images for testing. For our method, we hold out 1,000 training images as the validation data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | The augmentation policy network is an MLP that takes the embedding of the transformation and the corresponding augmented image feature as inputs, each followed by a fully-connected layer of size 100 with Re LU nonlinearities. The two intermediate features are then concatenated together, followed by a fully-connected output layer of size 1. The Sigmoid function is applied to the output. We also normalize the output weights of training samples in each mini-batch, i.e., each weight is divided by the sum of all weights in the mini-batch. We use K = 14 image processing functions: Auto Contrast, Equalize, Rotate, Posterize, Solarize, Color, Contrast, Brightness, Sharpness, Shear X/Y, Translate X/Y, Identity. For Meta Augment, the transformation is applied after horizontal flipping, and then Cutout (De Vries and Taylor 2017) with 16 16 pixels is applied. For each iteration, a mini-batch of training data Dtr mi = {(xi, yi)}ntr i=1 with batch size ntr is sampled and for each xi, a transformation T m1,m2 ji,ki is sampled to augment xi. The outer loop updates of θ and α are formulated by (θ(t+1), α(t+1)) = (θ(t), α(t)) β i =1 (θ,α)Lval i ( ˆw(t)(θ(t), α(t))), where β is a learning rate. |