Transformer-Explainability VS tf-metal-experiments

Compare Transformer-Explainability vs tf-metal-experiments and see what are their differences.

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
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tf-metal-experiments

TensorFlow Metal Backend on Apple Silicon Experiments (just for fun) (by tlkh)
GPU Deep Learning Tensorflow M1 m1-max Benchmark Bert
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Transformer-Explainability tf-metal-experiments
1 5
1,664 259
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0.0 0.0
3 months ago about 2 years ago
Jupyter Notebook Jupyter Notebook
MIT License MIT License
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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

tf-metal-experiments

Posts with mentions or reviews of tf-metal-experiments. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-28.

What are some alternatives?

When comparing Transformer-Explainability and tf-metal-experiments you can also consider the following projects:

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

MetalPetal - A GPU accelerated image and video processing framework built on Metal.

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

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

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

adanet - Fast and flexible AutoML with learning guarantees.

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

ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.

Transformer-Explainability vs pytorch-grad-cam tf-metal-experiments vs MetalPetal Transformer-Explainability vs shap tf-metal-experiments vs HugsVision Transformer-Explainability vs T2T-ViT tf-metal-experiments vs adanet Transformer-Explainability vs multi-label-sentiment-classifier tf-metal-experiments vs ML-Workspace