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1. Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data

   Guo, Peikun; Yang, Huiyuan; Sano, Akane (January 2023, The 11th IEEE International Conference on Healthcare Informatics)

   Data augmentation is a common practice to help generalization in the procedure of deep model training. In the context of physiological time series classification, previous research has primarily focused on label-invariant data augmentation methods. However, another class of augmentation techniques (i.e., Mixup) that emerged in the computer vision field has yet to be fully explored in the time series domain. In this study, we systematically review the mix-based augmentations, including mixup, cutmix, and manifold mixup, on six physio- logical datasets, evaluating their performance across different sensory data and classification tasks. Our results demonstrate that the three mix-based augmentations can consistently improve the performance on the six datasets. More importantly, the improvement does not rely on expert knowledge or extensive parameter tuning. Lastly, we provide an overview of the unique properties of the mix-based augmentation methods and highlight the potential benefits of using the mix-based augmentation in physiological time series data. Our code and results are available at https://github.com/comp-well-org/ Mix-Augmentation-for-Physiological-Time-Series-Classification. 

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2. Deep Augmentation: Dropout as Augmentation for Self-Supervised Learning

   Gabrielsson, Rickard; Wang, Tongzhou; Baradad, Manel; Solomon, Justin (May 2025, Transactions on machine learning research)

   Despite dropout’s ubiquity in machine learning, its effectiveness as a form of data augmentation remains under-explored. We address two key questions: (i) When is dropout effective as an augmentation strategy? (ii) Is dropout uniquely effective under these conditions? To explore these questions, we propose Deep Augmentation, a network- and modality-agnostic method that applies dropout or PCA transformations to targeted layers in neural networks. Through extensive experiments on contrastive learning tasks in NLP, computer vision, and graph learning, we find that uniformly applying dropout across layers does not consistently improve performance. Instead, dropout proves most beneficial in deeper layers and can be matched by alternative augmentations (e.g., PCA). We also show that a stop-gradient operation is critical for ensuring dropout functions effectively as an augmentation, and that performance trends invert when moving from contrastive tasks to supervised tasks. Our analysis suggests that Deep Augmentation helps mitigate inter-layer co-adaptation---a notable issue in self-supervised learning due to the absence of labeled data. Drawing on these insights, we outline a procedure for selecting the optimal augmentation layer and demonstrate that Deep Augmentation can outperform traditional input-level augmentations. This simple yet powerful approach can be seamlessly integrated into a wide range of architectures and modalities, yielding notable gains in both performance and generalization. 

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3. USB: A Unified Summarization Benchmark Across Tasks and Domains

   https://doi.org/10.18653/v1/2023.findings-emnlp.592  

   Krishna, Kundan; Gupta, Prakhar; Ramprasad, Sanjana; Wallace, Byron; Bigham, Jeffrey; Lipton, Zachary (January 2023, Proceedings of the Findings of the Association for Computational Linguistics (EMNLP))

   While the NLP community has produced numerous summarization benchmarks, none provide the rich annotations required to simultaneously address many important problems related to control and reliability. We introduce a Wikipedia-derived benchmark, complemented by a rich set of crowd-sourced annotations, that supports 8 interrelated tasks: (i) extractive summarization; (ii) abstractive summarization; (iii) topic-based summarization; (iv) compressing selected sentences into a one-line summary; (v) surfacing evidence for a summary sentence; (vi) predicting the factual accuracy of a summary sentence; (vii) identifying unsubstantiated spans in a summary sentence; (viii) correcting factual errors in summaries. We compare various methods on this benchmark and discover that on multiple tasks, moderately-sized fine-tuned models consistently outperform much larger few-shot prompted language models. For factuality-related tasks, we also evaluate existing heuristics to create training data and find that training on them results in worse performance than training on 20× less human-labeled data. Our articles draw from 6 domains, facilitating cross-domain analysis. On some tasks, the amount of training data matters more than the domain where it comes from, while for other tasks training specifically on data from the target domain, even if limited, is more beneficial. 

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4. Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory

   Chen, Hannah; Ji, Yangfeng; Evans, David (January 2020, Findings of ACL: Empirical Methods in Natural Language Processing)

   Most NLP datasets are manually labeled, so suffer from inconsistent labeling or limited size. We propose methods for automatically improving datasets by viewing them as graphs with expected semantic properties. We construct a paraphrase graph from the provided sentence pair labels, and create an augmented dataset by directly inferring labels from the original sentence pairs using a transitivity property. We use structural balance theory to identify likely mislabelings in the graph, and flip their labels. We evaluate our methods on paraphrase models trained using these datasets starting from a pretrained BERT model, and find that the automatically-enhanced training sets result in more accurate models. 

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5. Improving NLP Model Performance on Small Educational Data Sets Using Self-Augmentation

   https://doi.org/10.5220/0011857200003470  

   Cochran, Keith; Cohn, Clayton; Hastings, Peter (April 2023, scitepress.org)

   Jovanovic, Jelena; Chounta, Irene-Angelica; Uhomoibhi, James; McLaren, Bruce (Ed.)

   Computer-supported education studies can perform two important roles. They can allow researchers to gather important data about student learning processes, and they can help students learn more efficiently and effectively by providing automatic immediate feedback on what the students have done so far. The evaluation of student work required for both of these roles can be relatively easy in domains like math, where there are clear right answers. When text is involved, however, automated evaluations become more difficult. Natural Language Processing (NLP) can provide quick evaluations of student texts. However, traditional neural network approaches require a large amount of data to train models with enough accuracy to be useful in analyzing student responses. Typically, educational studies collect data but often only in small amounts and with a narrow focus on a particular topic. BERT-based neural network models have revolutionized NLP because they are pre-trained on very large corpora, developing a robust, contextualized understanding of the language. Then they can be “fine-tuned” on a much smaller set of data for a particular task. However, these models still need a certain base level of training data to be reasonably accurate, and that base level can exceed that provided by educational applications, which might contain only a few dozen examples. In other areas of artificial intelligence, such as computer vision, model performance on small data sets has been improved by “data augmentation” — adding scaled and rotated versions of the original images to the training set. This has been attempted on textual data; however, augmenting text is much more difficult than simply scaling or rotating images. The newly generated sentences may not be semantically similar to the original sentence, resulting in an improperly trained model. In this paper, we examine a self-augmentation method that is straightforward and shows great improvements in performance with different BERT-based models in two different languages and on two different tasks that have small data sets. We also identify the limitations of the self-augmentation procedure. 

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