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Data Augmentation for Low-Level Vision: CutBlur and Mixture-of-Augmentation
Data augmentation (DA) is an effective way to improve the performance of deep networks. Unfortunately, current methods are mostly developed for...
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GRAMO: geometric resampling augmentation for monocular 3D object detection
Data augmentation is widely recognized as an effective means of bolstering model robustness. However, when applied to monocular 3D object detection,...
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Adaptive data augmentation for mandarin automatic speech recognition
Audio data augmentation is widely adopted in automatic speech recognition (ASR) to alleviate the overfitting problem. However, noise-based data...
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Meta generative image and text data augmentation optimization
This paper proposes a method called Meta Generative Data Augmentation Optimization (MGDAO) to overcome limited types of operations for the...
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A Survey of Synthetic Data Augmentation Methods in Machine Vision
The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image...
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GANs for Data Augmentation in Healthcare
Computer-Assisted Diagnostics (CAD) using Convolutional Neural Network (CNN) model has become an important technology in the medical industry,...
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NeighborMix data augmentation for image recognition
Data augmentation can effectively enrich the diversity of training datasets to improve the generalization ability of deep learning models. Existing...
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Data Augmentation, Labelling, and Imperfections Third MICCAI Workshop, DALI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings
This LNCS conference volume constitutes the proceedings of the 3rd International Workshop on
Data Augmentation, Labeling, and Imperfections (DALI...
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Data Augmentation for Traffic Classification
Data Augmentation (DA)—enriching training data by adding synthetic samples—is a technique widely adopted in Computer Vision (CV) and Natural Language...
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Acoustic data augmentation for small passive acoustic monitoring datasets
Training complex deep neural networks can result in overfitting when the networks are trained from random weight initialization on small datasets....
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An overview of mixing augmentation methods and augmentation strategies
Deep Convolutional Neural Networks have made an incredible progress in many Computer Vision tasks. This progress, however, often relies on the...
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Data augmentation and adversary attack on limit resources text classification
Data Augmentation and Adversary Attack in text are complex techniques based on the generation of new instances. This is performed by introducing some...
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CNN-based data augmentation for handwritten gurumukhi text recognition
Models depicting deep learning have shown sustainable growth in recognizing handwritten words written in various languages, but the major challenges...
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DynamicAug: Enhancing Transfer Learning Through Dynamic Data Augmentation Strategies Based on Model State
Transfer learning has made significant advancements, however, the issue of overfitting continues to pose a major challenge. Data augmentation has...
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Random Padding Data Augmentation
The convolutional neural network (CNN) learns the same object in different positions in images, which can improve the model recognition accuracy. An...
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Multi-view and multi-augmentation for self-supervised visual representation learning
In the real world, the appearance of identical objects depends on factors as varied as resolution, angle, illumination conditions, and viewing...
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WGAN for Data Augmentation
Large annotated data sets play an important role in deep learning models as they need a lot of data to be trained to resemble the real data...
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Comparison of simple augmentation transformations for a convolutional neural network classifying medical images
Simple image augmentation techniques, such as reflection, rotation, or translation, might work differently for medical images than they do for...
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Contrastive learning with text augmentation for text classification
Various contrastive learning models have been successfully applied to representation learning for downstream tasks. The positive samples used in...
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RandoMix: a mixed sample data augmentation method with multiple mixed modes
Data augmentation plays a crucial role in enhancing the robustness and performance of machine learning models across various domains. In this study,...
