loss-landscape VS Transformer-MM-Explainability

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

loss-landscape

Code for visualizing the loss landscape of neural nets (by tomgoldstein)

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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loss-landscape Transformer-MM-Explainability
2 3
2,642 704
- -
0.0 0.0
about 2 years ago 8 months ago
Python Jupyter Notebook
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

loss-landscape

Posts with mentions or reviews of loss-landscape. 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 loss-landscape and Transformer-MM-Explainability you can also consider the following projects:

TorchDrift - Drift Detection for your PyTorch Models

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

deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

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.

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

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

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

cockpit - Cockpit: A Practical Debugging Tool for Training Deep Neural Networks

WeightWatcher - The WeightWatcher tool for predicting the accuracy of Deep Neural Networks