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[Re] Pure Noise to the Rescue of Insufficient Data

Ryan Lee · Seungmin Lee

Great Hall & Hall B1+B2 (level 1) #1910
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Thu 14 Dec 3 p.m. PST — 5 p.m. PST


Scope of Reproducibility — We examine the main claims of the original paper [1], whichstates that in an image classification task with imbalanced training data, (i) using purenoise to augment minority‐class images encourages generalization by improving minority‐class accuracy. This method is paired with (ii) a new batch normalization layer thatnormalizes noise images using affine parameters learned from natural images, whichimproves the model’s performance. Moreover, (iii) this improvement is robust to vary‐ing levels of data augmentation. Finally, the authors propose that (iv) adding pure noiseimages can improve classification even on balanced training data.Methodology — We implemented the training pipeline from the description of the paperusing PyTorch and integrated authors’ code snippets for sampling pure noise imagesand batch normalizing noise and natural images separately. All of our experiments wererun on a machine from a cloud computing service with one NVIDIA RTX A5000 GraphicsCard and had a total computational time of approximately 432 GPU hours.Results — We reproduced the main claims that (i) oversampling with pure noise improvesgeneralization by improving the minority‐class accuracy, (ii) the proposed batch nor‐malization (BN) method outperforms baselines, (iii) and this improvement is robustacross data augmentations. Our results also support that (iv) adding pure noise imagescan improve classification on balanced training data. However, additional experimentssuggest that the performance improvement from OPeN may be more orthogonal to theimprovement caused by a bigger network or more complex data augmentation.What was easy — The code snippet in the original paper was thoroughly documented andwas easy to use. The authors also clearly documented most of the hyperparameters thatwere used in the main experiments.What was difficult — The repo linked in the original paper was not populated yet. As a re‐sult, we had to retrieve the CIFAR‐10‐LT dataset from previous works [2, 3], re‐implementWideResNet [4], and the overall training pipeline.Communication with original authors — We contacted the authors for clarifications on theimplementation details of the algorithm. Prior works had many important implemen‐tation details such as linear learning rate warmup or deferred oversampling, so we con‐firmed with the authors on whether these methods were used.

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