In 2016, I began to wonder whether I could do anything about this. Deep learning — which uses multilayered neural networks to elicit patterns from data — was getting really hot back then. There was ...

The research article studies the impact of data augmentation ... of deep learning, supervised machine learning techniques, such as support vector machines (SVMs) and traditional artificial neural ...

“First of all, it’s better quality results,” Sam Hamilton, Visa’s SVP of data and ... enabled by deep learning AI models that produce instant risk scores and automatically block bad ...

If you have a set of data items, the goal of anomaly detection ... when using LightGBM is wading through the dozens of parameters. The LGBMRegressor class/object has 19 parameters (num_leaves, ...

Abstract: Data augmentation is a crucial component of machine learning. In 2-D object detection tasks, it can significantly enhance the performance of detectors without increasing the inference cost.

Modern object detection algorithms rely heavily on deep learning models that have been trained end ... To train a more robust model for object detection, the conventional data augmentation method ...

Abstract: Detecting objects remains one of computer vision and image understanding applications’ most fundamental and challenging aspects. Significant advances in object detection have been achieved ...

Such data scarcity is often met in the data-driven methods such as deep learning ... using different backbones with or without the auxiliary binary mask on CVPPP 2017 dataset (Bold values denote the ...

Called Faster Objects, More Objects (FOMO), the new deep learning architecture can unlock new computer vision applications. Most object-detection ... they need a lot of data.