loss-landscape VS TorchDrift

Compare loss-landscape vs TorchDrift and see what are their differences.

loss-landscape

Code for visualizing the loss landscape of neural nets (by tomgoldstein)
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loss-landscape TorchDrift
2 1
2,642 302
- 0.0%
0.0 0.0
about 2 years ago over 1 year ago
Python Jupyter Notebook
MIT License GNU General Public License v3.0 or later
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.

TorchDrift

Posts with mentions or reviews of TorchDrift. 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 TorchDrift you can also consider the following projects:

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.

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.

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

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

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

uncertainty-toolbox - Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

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

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

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