Summary of Global Clipper: Enhancing Safety and Reliability Of Transformer-based Object Detection Models, by Qutub Syed Sha et al.
Global Clipper: Enhancing Safety and Reliability of Transformer-based Object Detection Models
by Qutub Syed Sha, Michael Paulitsch, Karthik Pattabiraman, Korbinian Hagn, Fabian Oboril, Cornelius Buerkle, Kay-Ulrich Scholl, Gereon Hinz, Alois Knoll
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: As transformer-based object detection models advance, they are poised to have a significant impact on critical sectors like autonomous vehicles and aviation. However, soft errors causing bit flips during inference have been shown to significantly alter DNN performance, resulting in faulty predictions. Traditional range restriction solutions for CNNs fall short for transformers. To address this issue, this study introduces the Global Clipper and Global Hybrid Clipper, effective mitigation strategies specifically designed for transformer-based models. These clipper methods significantly enhance their resilience to soft errors, reducing faulty inferences to ~ 0%. The paper also details extensive testing across over 64 scenarios involving two transformer models (DINO-DETR and Lite-DETR) and two CNN models (YOLOv3 and SSD) using three datasets, totalling approximately 3.3 million inferences, to comprehensively assess model robustness. Furthermore, the study explores unique aspects of attention blocks in transformers and their operational differences from CNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This research paper is about making sure that AI object detection models work correctly even when there are tiny errors during processing. These errors can cause big problems in important areas like self-driving cars and airplanes. Right now, there aren’t good solutions to fix these errors for some types of AI models called transformers. The study introduces two new methods to help fix this problem and make the AI models more reliable. They tested these methods on many different scenarios using three datasets and found that they can greatly reduce errors. The paper also talks about how transformers work differently than other types of AI models. |
Keywords
» Artificial intelligence » Attention » Cnn » Inference » Object detection » Transformer
