Part 8/10:
Unsupervised learning also finds application in mapping between two different domains. Techniques like cyclical GANs offer methods for translating images from one style to another (e.g., converting photographs to paintings), allowing for insights into cross-domain representations without corresponding pairs of labeled data points.
- Anomaly Detection:
The nature of unsupervised learning allows it to thrive in anomaly detection tasks. By modeling the typical patterns present in data, these methods can identify outliers or unusual instances effectively, providing valuable insights across diverse fields, including security, healthcare, and industrial applications.