AIX360 VS Transformer-MM-Explainability

Compare AIX360 vs Transformer-MM-Explainability and see what are their differences.

Transformer-MM-Explainability

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA. (by hila-chefer)
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AIX360 Transformer-MM-Explainability
2 3
1,533 709
2.0% -
8.2 0.0
2 months ago 8 months ago
Python Jupyter Notebook
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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AIX360

Posts with mentions or reviews of AIX360. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

Transformer-MM-Explainability

Posts with mentions or reviews of Transformer-MM-Explainability. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-28.

What are some alternatives?

When comparing AIX360 and Transformer-MM-Explainability you can also consider the following projects:

AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

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

explainable-cnn - 📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

TorchDrift - Drift Detection for your PyTorch Models

cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both

explainerdashboard - Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.

shap - A game theoretic approach to explain the output of any machine learning model.

awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

clip-italian - CLIP (Contrastive Language–Image Pre-training) for Italian

backpack - BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.

pytea - PyTea: PyTorch Tensor shape error analyzer