PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

Computer Science

• Anomaly Detection 100%
• Class Classification 80%
• Performance Gain 40%
• Detection Method 40%
• Regularization 20%
• Supervised Learning 20%
• Classification Problem 20%
• Segmentation Method 20%
• Transfer Learning 20%
• Multi Class Classification 20%
• Multiclass Classification 20%
• Representation Learning 20%
• Classification Method 20%
• Deep Feature 20%
• Target Distribution 20%
• Continual Learning 20%

Keyphrases

• Pre-trained Features 100%
• High-quality Features 25%
• Self-supervised Learning 25%
• Collapse Features 25%
• Deep Support Vector Data Description (Deep SVDD) 25%
• Specialized Architectures 25%