articulated-animation
shap
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| articulated-animation | shap | |
|---|---|---|
| 4 | 38 | |
| 1,182 | 21,580 | |
| 2.6% | 1.8% | |
| 3.7 | 9.4 | |
| about 2 months ago | 6 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 or later | MIT License |
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articulated-animation
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✨ Best Computer Vision Projects with Source Code 🚀
🔗 https://github.com/snap-research/articulated-animation
- Motion Representations for Articulated Animation (2021)
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[D] SOTA Image Animation from Video?
I saw a post possibly on here or /r/futurology a few days ago that showed some pretty amazing results for animating images using a driving video, like this from Snap Research. I didn't save the post and wanted to read the paper, but was also wondering if this is still an active research area or not. It felt like lots of work was being done a couple years ago but I haven't seen much lately.
- Code for Motion Representations for Articulated Animation
shap
- Shap v0.45.0
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[D] Convert a ML model into a rule based system
something like GitHub - shap/shap: A game theoretic approach to explain the output of any machine learning model.?
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[P] tinyshap: A minimal implementation of the SHAP algorithm
A less than 100 lines of code implementation of KernelSHAP because I had a hard time understanding shap's code.
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What’s after model adequacy?
We use tools like SHAP to explain what the model is doing to stakeholders.
- Feature importance with feature engineering?
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Model interpretation with many features
https://github.com/slundberg/shap this or https://github.com/marcotcr/lime would be relevant to you, especially if you want to look at explaining a single prediction.
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SHAP Value Interpretation
See this closed topic for more detail: https://github.com/slundberg/shap/issues/29
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Christoph Molnar on SHAP Library
Dr. Molnar recently had a semi-viral post on LinkedIn and on Twitter, where he essentially highlights the booming popularity [and power] of using SHAP for explainable AI (which I agree with), but that it also comes with problems; i.e., the open source implementation has thousands of pull requests, bugs, and issues and yet there is no permanent or significant funding to go in and fix them.
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Random Forest Estimation Question
Option 4) create SHAP values https://github.com/slundberg/shap to better understand what the RF did.
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Model explainability
txtai pipelines are wrappers around Hugging Face pipelines with logic to easily integrate with txtai's workflow framework. Given that, we can use the SHAP library to explain predictions.
What are some alternatives?
first-order-model - This repository contains the source code for the paper First Order Motion Model for Image Animation
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Thin-Plate-Spline-Motion-Model - [CVPR 2022] Thin-Plate Spline Motion Model for Image Animation.
Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
fastbook - The fastai book, published as Jupyter Notebooks
captum - Model interpretability and understanding for PyTorch
VRT - VRT: A Video Restoration Transformer (official repository)
lime - Lime: Explaining the predictions of any machine learning classifier
RelTR - RelTR: Relation Transformer for Scene Graph Generation: https://arxiv.org/abs/2201.11460v2
interpret - Fit interpretable models. Explain blackbox machine learning.
JoJoGAN - Official PyTorch repo for JoJoGAN: One Shot Face Stylization
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning