Self-Supervised Contextual Bandits in Computer Vision

Understand

Contextual bandits are a common problem faced by machine learning practitioners in domains as diverse as hypothesis testing to product recommendations.

• There have been a lot of approaches in exploiting rich data representations for contextual bandit problems with varying degree of success.
• Self-supervised learning is a promising approach to find rich data representations without explicit labels.
• In a typical self-supervised learning scheme, the primary task is defined by the problem objective (e.g.