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Data-centric AI/Data augmentation

Ryan Lee, Seungmin Lee

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.

Data-centric AI/Data augmentation

Guozheng Ma, Linrui Zhang, Haoyu Wang, Lu Li, Zilin Wang, Zhen Wang, Li Shen, Xueqian Wang, Dacheng Tao

Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms.Notably, employing simple observation transformations alone can yield outstanding performance without extra auxiliary representation tasks or pre-trained encoders. However, it remains unclear which attributes of DA account for its effectiveness in achieving sample-efficient visual RL. To investigate this issue and further explore the potential of DA, this work conducts comprehensive experiments to assess the impact of DA's attributes on its efficacy and provides the following insights and improvements: (1) For individual DA operations, we reveal that both ample spatial diversity and slight hardness are indispensable. Building on this finding, we introduce Random PadResize (Rand PR), a new DA operation that offers abundant spatial diversity with minimal hardness. (2) For multi-type DA fusion schemes, the increased DA hardness and unstable data distribution result in the current fusion schemes being unable to achieve higher sample efficiency than their corresponding individual operations. Taking the non-stationary nature of RL into account, we propose a RL-tailored multi-type DA fusion scheme called Cycling Augmentation (CycAug), which performs periodic cycles of different DA operations to increase type diversity while maintaining data distribution consistency. Extensive evaluations on the DeepMind Control suite and CARLA driving simulator demonstrate that our methods achieve superior sample efficiency compared with the prior state-of-the-art methods.

Data-centric AI/Data augmentation

Zhi Li, Yifan Liu, Yin Zhang

We introduce Back-Modality, a novel data augmentation schema predicated on modal transformation. Data from an initial modality undergoes transformation to an intermediate modality, followed by a reverse transformation. This framework serves dual roles. On one hand, it operates as a general data augmentation strategy. On the other hand, it allows for other augmentation techniques, suitable for the intermediate modality, to enhance the initial modality. For instance, data augmentation methods applicable to pure text can be employed to augment images, thereby facilitating the cross-modality of data augmentation techniques. To validate the viability and efficacy of our framework, we proffer three instantiations of Back-Modality: back-captioning, back-imagination, and back-speech. Comprehensive evaluations across tasks such as image classification, sentiment classification, and textual entailment demonstrate that our methods consistently enhance performance under data-scarce circumstances.

Data-centric AI/Data augmentation

Lisa Dunlap, Alyssa Umino, Han Zhang, Jiezhi Yang, Joseph Gonzalez, Trevor Darrell

Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data. We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing. To maintain data integrity, a model trained on the original dataset filters out minimal image edits and those which corrupt class-relevant information. The resulting dataset is visually consistent with the original training data and offers significantly enhanced diversity. We show that ALIA is able to surpasses traditional data augmentation and text-to-image generated data on fine-grained classification tasks, including cases of domain generalization and contextual bias. Code is available at https://github.com/lisadunlap/ALIA.