shap VS Transformer-Explainability

Compare shap vs Transformer-Explainability and see what are their differences.

shap

A game theoretic approach to explain the output of any machine learning model. (by shap)
Interpretability Machine Learning Deep Learning gradient-boosting Shap Shapley explainability
Source Code
shap.readthedocs.io
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Transformer-Explainability

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks. (by hila-chefer)
Deep Learning vision-transformer bert-model Bert explainability transformer-interpretability perturbation attention-visualization visualize-classifications vit attention-matrix cvpr2021
Source Code
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shap Transformer-Explainability
38 1
21,580 1,660
1.8% -
9.4 0.0
4 days ago 3 months ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
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shap

Posts with mentions or reviews of shap. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-06.

Transformer-Explainability

Posts with mentions or reviews of Transformer-Explainability. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-25.
  • [Project] Recent Class Activation Map Methods for CNNs and Vision Transformers
    2 projects | /r/MachineLearning | 25 Apr 2021
    Not exactly the same but since you mentioned using ViT's attention outputs as a 2D feature map for the CAM you can consider this paper (Transformer Interpretability Beyond Attention Visualization) where they study the question of how to choose/mix the attention scores in a way that can be visualized (so similar to the CAMs). Maybe it can lead to better results. https://arxiv.org/abs/2012.09838 https://github.com/hila-chefer/Transformer-Explainability

What are some alternatives?

When comparing shap and Transformer-Explainability you can also consider the following projects:

shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

captum - Model interpretability and understanding for PyTorch

T2T-ViT - ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

lime - Lime: Explaining the predictions of any machine learning classifier

multi-label-sentiment-classifier - How to build a multi-label sentiment classifiers with Tez and PyTorch

interpret - Fit interpretable models. Explain blackbox machine learning.

HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision

awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

tf-metal-experiments - TensorFlow Metal Backend on Apple Silicon Experiments (just for fun)

anchor - Code for "High-Precision Model-Agnostic Explanations" paper

deep-text-recognition-benchmark - PyTorch code of my ICDAR 2021 paper Vision Transformer for Fast and Efficient Scene Text Recognition (ViTSTR)

shap vs shapash Transformer-Explainability vs pytorch-grad-cam shap vs captum Transformer-Explainability vs T2T-ViT shap vs lime Transformer-Explainability vs multi-label-sentiment-classifier shap vs interpret Transformer-Explainability vs HugsVision shap vs awesome-production-machine-learning Transformer-Explainability vs tf-metal-experiments shap vs anchor Transformer-Explainability vs deep-text-recognition-benchmark