Glitch Classification

Glitch classification focuses on automatically identifying and categorizing anomalous events or artifacts ("glitches") within various data streams, aiming to improve data quality and system reliability. Current research employs diverse approaches, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based models, often leveraging techniques like transfer learning and unsupervised dimensionality reduction for improved efficiency and performance. This field is crucial for enhancing the trustworthiness of large language models, improving video game development processes, and advancing gravitational wave detection by filtering out noise, ultimately leading to more robust and reliable systems across multiple scientific and technological domains.