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[CVPR 2025 Workshop] SK-RD4AD: Skip-Connected Reverse Distillation for One-Class Anomaly Detection

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SK-RD4AD: Skip-Connected Reverse Distillation for One-Class Anomaly Detection

Welcome to the repository for SK-RD4AD! This repository focuses on Skip-Connected Reverse Distillation for One-Class Anomaly Detection, a technique developed for the CVPR 2025 Workshop. If you are interested in anomaly detection, computer vision, industrial AI, one-class classification, skip connections, or vision AI, this repository is the place to be.

Overview

SK-RD4AD is a novel approach to anomaly detection that leverages skip connections and reverse distillation to achieve superior performance in identifying anomalies in industrial settings. The technique has been specifically designed to address the challenges faced in one-class anomaly detection, providing a robust solution that can be applied to a variety of computer vision tasks.

Key Features

  • Skip Connections: By incorporating skip connections within the architecture, SK-RD4AD enables the model to capture both low-level and high-level features, enhancing the ability to detect anomalies effectively.

  • Reverse Distillation: The use of reverse distillation ensures that the model can distill knowledge learned from a teacher model, improving the overall performance of anomaly detection in real-world scenarios.

How to Use

To get started with SK-RD4AD, visit the Releases section and download the necessary files. Once you have the files, follow the instructions provided to execute the code and start exploring the capabilities of SK-RD4AD.

Additional Resources

For more information on the development and implementation of SK-RD4AD, please refer to the documentation included in the repository. If you have any questions or suggestions, feel free to raise an issue or reach out to the project contributors.

Join the Community

We welcome contributions from the community to enhance and improve SK-RD4AD further. If you are passionate about anomaly detection, computer vision, or AI in industrial applications, we encourage you to get involved and share your expertise.

Stay Updated

To stay updated on the latest developments and updates to SK-RD4AD, make sure to follow the repository and join our growing community of developers and researchers.

Thank you for your interest in SK-RD4AD. We look forward to seeing the impact of Skip-Connected Reverse Distillation on anomaly detection in the field of computer vision and beyond.

🔍🛠️📊


Feel free to visit the Releases section to download and explore the project further.


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[CVPR 2025 Workshop] SK-RD4AD: Skip-Connected Reverse Distillation for One-Class Anomaly Detection

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