Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
普通的機器學習分類迴歸問題都是點估計，即模型給出的輸出是一個real value，或者是各個類別的probability。 但是模型對於不同點的估計，確信度應該是不同的。對於和以往出現過樣本非常相似的點，給出的預測確信度比較高，對於和以往出現過點距離比較遠的點的估計，確信度應該降低。 目前衡量神經網絡結果不確定性的方法主要是貝葉斯神經網絡，每一層的w和b都是服從一個分佈而非是確定的值，所以自然，最
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相關文章
- 1. 3D Bounding Box Estimation Using Deep Learning and Geometry
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- 5. 論文翻譯：2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning
- 6. 姿態估計「2D/3D Pose Estimation and Action Recognition using Multitask Deep Learning」
- 7. Deep Learning Models and Code for Pose Estimation
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- 9. Simple Baselines for Human Pose Estimation and Tracking
- 10. Underexposed Photo Enhancement using Deep Illumination Estimation（DeepUPE）筆記