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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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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 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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Evaluation of data augmentation techniques on subjective tasks
Data augmentation is widely applied in various computer vision problems for artificially increasing the size of a dataset by transforming the...
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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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Improving plant disease classification using realistic data augmentation
Recently, several studies have used deep convolutional neural networks (DCNN) for plant disease classification based on leaf symptoms images. Most of...
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A systematic study of data augmentation for protected AES implementations
Side-channel attacks against cryptographic implementations are mitigated by the application of masking and hiding countermeasures. Hiding...
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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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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 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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Alarm Log Data Augmentation Algorithm Based on a GAN Model and Apriori
The complexity of alarm detection and diagnosis tasks often results in a lack of alarm log data. Due to the strong rule associations inherent in...
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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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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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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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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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Graph contrastive learning for recommendation with generative data augmentation
Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures...
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PatchMix: patch-level mixup for data augmentation in convolutional neural networks
Convolutional neural networks (CNNs) have demonstrated impressive performance in fitting data distribution. However, due to the complexity in...
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Leveraging Data Augmentation for Process Information Extraction
Business Process Modeling projects often require formal process models as a central component. High costs associated with the creation of such formal...
