Unsupervised learning methods are becoming increasingly important as the field of computer vision evolves. Without being given predefined categories or target outputs, unsupervised learning algorithms try to find patterns or representations in the data.

From unraveling hidden structures in data to propelling advancements in image segmentation and dimensionality reduction, unsupervised learning is the catalyst for innovation. This workshop sheds light on its impact, focusing on tasks such as anomaly detection that have transformative implications. It is intended to give participants a brief understanding of unsupervised learning techniques and their applications in computer vision, as well as hands-on exercises on anomaly detection in images.

Objectives:

Gain a foundational understanding of unsupervised learning in the context of computer vision applications.

Explore the fascinating realm of anomaly detection—a skill essential for identifying outliers, uncovering fraud, rectifying data errors, and finding unusual patterns. Become the detective of the data world!

Dive into hands-on exercises where participants will implement and experiment with unsupervised anomaly detection in medical images using PyTorch.

Level: Intermediate

Duration: 2 hours

Prerequisites:

A basic understanding of machine learning concepts as well as experience with Python programming are essential. Experience with a machine learning library, such as PyTorch is required. Participants are encouraged to bring their laptops for an engaging and immersive learning experience.

Unsupervised Learning in Computer Vision