Summary of Entropy Loss: An Interpretability Amplifier Of 3d Object Detection Network For Intelligent Driving, by Haobo Yang et al.
Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent Driving
by Haobo Yang, Shiyan Zhang, Zhuoyi Yang, Xinyu Zhang, Li Wang, Yifan Tang, Jilong Guo, Jun Li
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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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 This paper proposes a novel loss function, Entropy Loss, and an innovative training strategy to improve the interpretability of deep learning-based intelligent driving perception models. The Entropy Loss is formulated based on feature compression networks within the perception model, drawing inspiration from communication systems. By modeling network layer outputs as continuous random variables, the authors construct a probabilistic model that quantifies changes in information volume and derive the Entropy Loss to guide network parameter updates. The results show that the Entropy Loss training strategy accelerates the training process, with improved 3D object detection model accuracy on the KITTI test set by up to 4.47% compared to models without Entropy Loss. This work highlights the significance of interpretability in intelligent driving perception and demonstrates the effectiveness of Entropy Loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make self-driving cars safer by making them understand what they are seeing better. Right now, these cars use deep learning, which is like a black box – we don’t really know how it works. The authors propose a new way to train these models that makes them more understandable and improves their performance. They tested this method on a dataset of 3D object detection and showed that it worked better than the traditional method. This could lead to safer and more reliable self-driving cars in the future. |
Keywords
» Artificial intelligence » Deep learning » Loss function » Object detection » Probabilistic model
