Awesome-Dataset-Distillation
Awesome Dataset Distillation Papers (by Guang000)
walk_in_the_park
By ikostrikov
Awesome-Dataset-Distillation | walk_in_the_park | |
---|---|---|
3 | 2 | |
1,176 | 226 | |
- | - | |
9.6 | 0.0 | |
2 days ago | over 1 year ago | |
HTML | Python | |
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.
Awesome-Dataset-Distillation
Posts with mentions or reviews of Awesome-Dataset-Distillation.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-09-03.
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Researchers created a Novel Framework called ‘FedD3’ for Federated Learning in Resource-Constrained Edge Environments via Decentralized Dataset Distillation
Continue Reading | Check out the paper and github link.
- [D] Most Popular AI Research Aug 2022 - Ranked Based On GitHub Stars
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Most Popular AI Research Aug 2022 pt. 2 - Ranked Based On GitHub Stars
https://arxiv.org/abs/2208.11311 https://github.com/Guang000/Awesome-Dataset-Distillation
walk_in_the_park
Posts with mentions or reviews of walk_in_the_park.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-09-03.
What are some alternatives?
When comparing Awesome-Dataset-Distillation and walk_in_the_park you can also consider the following projects:
textual_inversion
Intrusion-Detection-System-Using-Machine-Learning - Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
Cold-Diffusion-Models - Official implementation of Cold-Diffusion for different transformations in pytorch.
VideoX - VideoX: a collection of video cross-modal models
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
PeRFception - [NeurIPS2022] Official implementation of PeRFception: Perception using Radiance Fields.
MinVIS
Awesome-Dataset-Distillation vs textual_inversion
walk_in_the_park vs textual_inversion
Awesome-Dataset-Distillation vs Intrusion-Detection-System-Using-Machine-Learning
walk_in_the_park vs Cold-Diffusion-Models
Awesome-Dataset-Distillation vs VideoX
walk_in_the_park vs bitsandbytes
Awesome-Dataset-Distillation vs PeRFception
walk_in_the_park vs MinVIS
Awesome-Dataset-Distillation vs bitsandbytes
walk_in_the_park vs PeRFception
Awesome-Dataset-Distillation vs MinVIS
walk_in_the_park vs Intrusion-Detection-System-Using-Machine-Learning