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What is data augmentation? | IBM

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Read the guideExplore watsonx OrchestrateExplore AI development toolsExplore AI servicesExplore watsonx OrchestrateExplore watsonx.aiData Augmentation Can Improve RobustnessTackling class imbalance in computer vision: A contemporary review,” Artificial Intelligence ReviewData Augmentation for Skin Lesion AnalysisA survey on Image Data Augmentation for Deep LearningA Comprehensive Survey of Image Augmentation Techniques for Deep LearningA survey on Image Data Augmentation for Deep LearningImproved Regularization of Convolutional Neural Networks with CutoutTowards Instance-Level Image-To-Image TranslationSimple Copy-Paste Is a Strong Data Augmentation Method for Instance SegmentationText Data Augmentation for Deep LearningSyntactic Data Augmentation Increases Robustness to Inference HeuristicsText Data Augmentation for Deep LearningImproving Neural Machine Translation Models with Monolingual DataText Data Augmentation for Deep LearningMixup-Transformer: Dynamic Data Augmentation for NLP TasksAugmenting Data with Mixup for Sentence Classification: An Empirical StudyImage Data Augmentation for Deep Learning: A SurveyData augmentation: A comprehensive survey of modern approachesMedical image data augmentation: techniques, comparisons and interpretationsAutoAugment: Learning Augmentation Strategies From DataLearning Data Augmentation Strategies for Object DetectionExplanation-based Data Augmentation for Image ClassificationSemi-supervised and Task-Driven Data AugmentationADDA: Class-wise Automatic Differentiable Data Augmentation for EEG SignalsGAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classificationData augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasksGPT3Mix: Leveraging Large-scale Language Models for Text AugmentationAugGPT: Leveraging ChatGPT for Text Data AugmentationAnalysis of Training Object Detection Models with Synthetic DataObject Detector Differences When Using Synthetic and Real Training DataUnsupervised Writer Adaptation for Synthetic-to-Real Handwritten Word Recognition

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