Latent Sculpting for Out-of-Distribution Anomaly Detection: A Novel Approach

This research explores a novel method for anomaly detection using latent space sculpting. The focus on zero-shot generalization is particularly relevant for real-world scenarios where unseen data is common.

• •Proposes a novel manifold learning approach for anomaly detection.
• •Addresses the challenge of zero-shot generalization.
• •Potentially applicable in various domains facing out-of-distribution data.