continual-pretraining-nlp-vision
Code to reproduce experiments from the paper "Continual Pre-Training Mitigates Forgetting in Language and Vision" https://arxiv.org/abs/2205.09357 (by AndreaCossu)
awesome-machine-unlearning
Awesome Machine Unlearning (A Survey of Machine Unlearning) (by tamlhp)
continual-pretraining-nlp-vision | awesome-machine-unlearning | |
---|---|---|
1 | 5 | |
14 | 609 | |
- | - | |
4.8 | 7.9 | |
7 months ago | 28 days ago | |
Jupyter Notebook | 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.
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.
continual-pretraining-nlp-vision
Posts with mentions or reviews of continual-pretraining-nlp-vision.
We have used some of these posts to build our list of alternatives
and similar projects.
-
[2205.09357] Continual Pre-Training Mitigates Forgetting in Language and Vision
Code for https://arxiv.org/abs/2205.09357 found: https://github.com/AndreaCossu/continual-pretraining-nlp-vision
awesome-machine-unlearning
Posts with mentions or reviews of awesome-machine-unlearning.
We have used some of these posts to build our list of alternatives
and similar projects.
-
[P] [R] Machine Unlearning Summary
Github Repo: https://github.com/tamlhp/awesome-machine-unlearning 📚 Notebook: https://www.kaggle.com/code/tamlhp/machine-unlearning-the-right-to-be-forgotten/
-
[R] A Survey of Machine Unlearning
Today, computer systems hold large amounts of personal data. Yet while such an abundance of data allows breakthroughs in artificial intelligence, and especially machine learning (ML), its existence can be a threat to user privacy, and it can weaken the bonds of trust between humans and AI. Recent regulations now require that, on request, private information about a user must be removed from both computer systems and from ML models, i.e. ``the right to be forgotten''). While removing data from back-end databases should be straightforward, it is not sufficient in the AI context as ML models often `remember' the old data. Contemporary adversarial attacks on trained models have proven that we can learn whether an instance or an attribute belonged to the training data. This phenomenon calls for a new paradigm, namely machine unlearning, to make ML models forget about particular data. It turns out that recent works on machine unlearning have not been able to completely solve the problem due to the lack of common frameworks and resources. Therefore, this paper aspires to present a comprehensive examination of machine unlearning's concepts, scenarios, methods, and applications. Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications. In addition, we aim to highlight the key findings, current trends, and new research areas that have not yet featured the use of machine unlearning but could benefit greatly from it. We hope this survey serves as a valuable resource for ML researchers and those seeking to innovate privacy technologies. Our resources are publicly available at this https URL.
-
Welcome!
Welcome to Machine unlearning, You can post all kinds of stuff about Machine unlearning here . Here is a great resource to get you started https://github.com/tamlhp/awesome-machine-unlearning
-
[P] [R] [D] Can Machine Actually Forget Your Data?
We also have a Github repo for this topic, please consider star if this topic piques your curiosity.
- [P] Awesome Machine Unlearning
What are some alternatives?
When comparing continual-pretraining-nlp-vision and awesome-machine-unlearning you can also consider the following projects:
gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
differential-privacy-library - Diffprivlib: The IBM Differential Privacy Library
vision-transformer-from-scratch - A Simplified PyTorch Implementation of Vision Transformer (ViT)
AIJack - Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
fides - The Privacy Engineering & Compliance Framework
course-content-dl - NMA deep learning course
PyRedactKit - Python CLI tool to redact and un-redact sensitive data from text files. 🔐📝
continual-pretraining-nlp-vision vs gan-vae-pretrained-pytorch
awesome-machine-unlearning vs differential-privacy-library
continual-pretraining-nlp-vision vs vision-transformer-from-scratch
awesome-machine-unlearning vs AIJack
awesome-machine-unlearning vs fides
awesome-machine-unlearning vs course-content-dl
awesome-machine-unlearning vs PyRedactKit