Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision | Qualitative Results
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5 سال پیش
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Qualitative results for the paper:Evaluating
Qualitative results for the paper:
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision, 2019.
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön.
arXiv: https://arxiv.org/abs/1906.01620
Code: https://github.com/fregu856/evaluatin...
Project page: http://www.fregu856.com/publication/e...
We propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. It is specifically designed to test the robustness required in real-world computer vision applications. We also apply our proposed framework to provide the first properly extensive and conclusive comparison of the two current state-of-the-art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates.
- All shown results are for ensembling with M = 8.
- Street-scene semantic segmentation: 0:00 - 8:22.
- - Cityscapes to Cityscapes (real to real): 0:00.
- - Synscapes to Cityscapes (synthetic to real): 2:30.
- - Synscapes to Synscapes (synthetic to synthetic): 5:00.
- - Cityscapes to Synscapes (real to synthetic): 6:41
- Depth completion: 8:22 - 14:18.
- - virtual KITTI to virtual KITTI (synthetic to synthetic): 8:22.
- - virtual KITTI to KITTI (synthetic to real): 9:26.
- On Cityscapes, the input image, prediction and predictive entropy are visualized.
- On Synscapes, the input image, ground truth, prediction and predictive entropy are visualized.
- For depth completion, the input image, input sparse depth map, ground truth depth map, prediction, predictive uncertainty, aleatoric uncertainty and epistemic uncertainty are visualized.
- Black: minimum uncertainty, white: maximum uncertainty.
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision, 2019.
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön.
arXiv: https://arxiv.org/abs/1906.01620
Code: https://github.com/fregu856/evaluatin...
Project page: http://www.fregu856.com/publication/e...
We propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. It is specifically designed to test the robustness required in real-world computer vision applications. We also apply our proposed framework to provide the first properly extensive and conclusive comparison of the two current state-of-the-art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates.
- All shown results are for ensembling with M = 8.
- Street-scene semantic segmentation: 0:00 - 8:22.
- - Cityscapes to Cityscapes (real to real): 0:00.
- - Synscapes to Cityscapes (synthetic to real): 2:30.
- - Synscapes to Synscapes (synthetic to synthetic): 5:00.
- - Cityscapes to Synscapes (real to synthetic): 6:41
- Depth completion: 8:22 - 14:18.
- - virtual KITTI to virtual KITTI (synthetic to synthetic): 8:22.
- - virtual KITTI to KITTI (synthetic to real): 9:26.
- On Cityscapes, the input image, prediction and predictive entropy are visualized.
- On Synscapes, the input image, ground truth, prediction and predictive entropy are visualized.
- For depth completion, the input image, input sparse depth map, ground truth depth map, prediction, predictive uncertainty, aleatoric uncertainty and epistemic uncertainty are visualized.
- Black: minimum uncertainty, white: maximum uncertainty.
5 سال پیش
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