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hila-chefer avatar

hila-chefer/Transformer-MM-Explainability

0
View on GitHub↗
908 stars·116 forks·Jupyter Notebook·MIT·8 views

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.

Features

  • Representation Learning - Explainability for bi-modal and encoder-decoder transformers.

Star history

Star history chart for hila-chefer/transformer-mm-explainabilityStar history chart for hila-chefer/transformer-mm-explainability

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Frequently asked questions

What does hila-chefer/transformer-mm-explainability do?

[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.

What are the main features of hila-chefer/transformer-mm-explainability?

The main features of hila-chefer/transformer-mm-explainability are: Representation Learning.

Which projects share features with hila-chefer/transformer-mm-explainability?

Projects with overlapping indexed features include: angus924/minirocket — ROCKET · MINIROCKET · HYDRA. anhduy0911/coinception — The requirements.txt file are attached for list of packages required. Python 3.9.16 torch==2.0.0 scikitlearn==0.24.2… beckschen/transmix — This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transformers, CVPR 2022. boschresearch/continuous-recurrent-units. boschresearch/expclr — Paper published at ICML22. Link to our paper: https://icml.cc/virtual/2022/spotlight/18038. ahmedmalaa/fourier-flows — Code for Generative Time-series Modeling with Fourier Flows.

Projects sharing features with Transformer MM Explainability

These projects share indexed features with Transformer MM Explainability. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • angus924/minirocketangus924 avatar

    angus924/minirocket

    337View on GitHub↗

    ROCKET · MINIROCKET · HYDRA

    Python
    View on GitHub↗337
  • anhduy0911/coinceptionanhduy0911 avatar

    anhduy0911/CoInception

    8View on GitHub↗

    The requirements.txt file are attached for list of packages required. Python 3.9.16 torch==2.0.0 scikitlearn==0.24.2 pywavelets==1.4.1 pandas scipy statsmodels matplotlib Bottleneck

    Python
    View on GitHub↗8
  • beckschen/transmixBeckschen avatar

    Beckschen/TransMix

    157View on GitHub↗

    This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transformers, CVPR 2022

    Python
    View on GitHub↗157
  • ahmedmalaa/fourier-flowsA

    ahmedmalaa/Fourier-flows

    0View on GitHub↗

    Code for Generative Time-series Modeling with Fourier Flows.

    View on GitHub↗0
Compare all 30 related projects→