Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 EMAIL |
| 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. |