fortuna
surface_normal_uncertainty
| fortuna | surface_normal_uncertainty | |
|---|---|---|
| 5 | 1 | |
| 855 | 196 | |
| 1.9% | - | |
| 8.2 | 10.0 | |
| 21 days ago | over 1 year ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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fortuna
- 🚀 AWS launches Fortuna, an open-source library for Uncertainty Quantification
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[P] 🚀 AWS launches Fortuna, an open-source library for Uncertainty Quantification
What is the best end-to-end example showing it? https://github.com/awslabs/fortuna/blob/main/examples/mnist_classification.ipynb ? It would be nice to have some visual explainer, as in https://github.com/aangelopoulos/conformal_classification .
- AWS Fortuna, an open-source library for Uncertainty Quantification
surface_normal_uncertainty
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Non official colab to create normal maps using "Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation" from baegwangbin
I needed normal maps for my movie and I saw in the new ControlNet update that they used https://github.com/baegwangbin/surface_normal_uncertainty to make normals which gave me better results than previous methods. So I decided to make a colab to process all my images because I didn't find how to do it in Automatic 1111.
What are some alternatives?
uq-vae - Solving Bayesian Inverse Problems via Variational Autoencoders
2dimageto3dmodel - We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.
pytorch-forecasting - Time series forecasting with PyTorch
IGR - Implicit Geometric Regularization for Learning Shapes
deep-kernel-transfer - Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
ipme - An interactive visualization tool that transforms probabilistic programming models into an "Interactive Probabilistic Models Explorer".
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DIML - [ICCV 2021] Towards Interpretable Deep Metric Learning with Structural Matching
conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
neural-deferred-shading - Multi-View Mesh Reconstruction with Neural Deferred Shading (CVPR 2022)
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
calibrated-backprojection-network - PyTorch Implementation of Unsupervised Depth Completion with Calibrated Backprojection Layers (ORAL, ICCV 2021)