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)
Review Deep Learning vision-transformer bert-model Bert explainability transformer-interpretability perturbation attention-visualization visualize-classifications vit attention-matrix cvpr2021
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  • [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

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hila-chefer/Transformer-Explainability is an open source project licensed under MIT License which is an OSI approved license.

The primary programming language of Transformer-Explainability is Jupyter Notebook.

Popular Comparisons

  1. Transformer-Explainability VS pytorch-grad-cam
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  5. Transformer-Explainability VS tf-metal-experiments
  6. Transformer-Explainability VS deep-text-recognition-benchmark
  7. Transformer-Explainability VS HugsVision
  8. Transformer-Explainability VS eomt
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