Ne2Ne-Image-Denoising
Deep Unsupervised Image Denoising, based on Neighbour2Neighbour training (by neeraj3029)
pytorch-metric-learning
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch. (by KevinMusgrave)
| Ne2Ne-Image-Denoising | pytorch-metric-learning | |
|---|---|---|
| 1 | 3 | |
| 27 | 5,764 | |
| - | - | |
| 3.0 | 7.9 | |
| 11 months ago | about 1 month ago | |
| Python | Python | |
| - | 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.
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.
Ne2Ne-Image-Denoising
Posts with mentions or reviews of Ne2Ne-Image-Denoising.
We have used some of these posts to build our list of alternatives
and similar projects.
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Neighbour2Neighbour: The new self-supervised Image Denoising training
It gives outstanding denoising performance with just 300 training images. Have a look at these image results and minimal network implementation here: https://github.com/neeraj3029/Ne2Ne-Image-Denoising
pytorch-metric-learning
Posts with mentions or reviews of pytorch-metric-learning.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-07-13.
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Similarity Learning lacks a framework. So we built one
Not a full featured framework, but pytorch-metric-learning has data loaders, lossess, etc. to facilitate similarity learning: https://github.com/KevinMusgrave/pytorch-metric-learning
Disclaimer: I've made some contributions to it.
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[R][D] VAE Embedding Space - Can we force it to learn a metric?
You can use the triplet loss together with the Gaussian prior. It will be zero centered though and the clusters are not as separated when you use the triplet loss only.There are many alternative to the triplet loss, in case it needs to be a metric: https://github.com/KevinMusgrave/pytorch-metric-learning
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[D] Similar Image Retrieval
This repo provides the tools and examples needed to build such a model: https://github.com/KevinMusgrave/pytorch-metric-learning