methodology-4
Data Augmentation
3260 papers • 126 benchmarks • 313 datasets
Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting. Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others. Further readings: A Survey of Data Augmentation Approaches for NLP A survey on Image Data Augmentation for Deep Learning ( Image credit: Albumentations )
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YOLOv4: Optimal Speed and Accuracy of Object Detection
Chien-Yao Wang, Alexey Bochkovskiy, H. Liao•Wed Apr 22 2020
This work uses new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, C mBN, DropBlock regularization, and CIoU loss, and combine some of them to achieve state-of-the-art results: 43.5% AP for the MS COCO dataset at a realtime speed of ~65 FPS on Tesla V100.
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