Browse all content tagged with Data Augmentation
Data scarcity refers to insufficient data for training machine learning models or comprehensive analysis, hindering the development of accurate AI systems. Discover causes, impacts, and techniques to overcome data scarcity in AI and automation.
Few-Shot Learning is a machine learning approach that enables models to make accurate predictions using only a small number of labeled examples. Unlike traditional supervised methods, it focuses on generalizing from limited data, leveraging techniques like meta-learning, transfer learning, and data augmentation.
A Generative Adversarial Network (GAN) is a machine learning framework with two neural networks—a generator and a discriminator—that compete to generate data indistinguishable from real data. Introduced by Ian Goodfellow in 2014, GANs are widely used for image generation, data augmentation, anomaly detection, and more.
