Awesome-Dataset-Distillation
Awesome Dataset Distillation Papers (by Guang000)
MinVIS
By NVlabs
| Awesome-Dataset-Distillation | MinVIS | |
|---|---|---|
| 3 | 5 | |
| 1,173 | 261 | |
| - | 0.0% | |
| 9.6 | 0.0 | |
| 14 days ago | over 1 year ago | |
| HTML | Python | |
| 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.
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.
-
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
-
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
MinVIS
Posts with mentions or reviews of MinVIS.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-09-03.
-
Using image based models for videos
This is basically that but for instance segmentation https://github.com/NVlabs/MinVIS
- [D] Most Popular AI Research Aug 2022 - Ranked Based On GitHub Stars
-
Most Popular AI Research Aug 2022 pt. 2 - Ranked Based On GitHub Stars
https://arxiv.org/abs/2208.02245 https://github.com/nvlabs/minvis
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NVIDIA AI Researchers Propose ‘MinVIS,’ A Minimal Video Instance Segmentation (VIS) Framework That Achieves SOTA Performance With Neither Video-Based Architectures Nor Training Procedures
Continue reading | Check out the Preprint/Under review paper and github link.
- A Minimal Video Instance Segmentation Framework Without Video-Based Training
What are some alternatives?
When comparing Awesome-Dataset-Distillation and MinVIS 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.
walk_in_the_park
Awesome-Dataset-Distillation vs textual_inversion
MinVIS vs Intrusion-Detection-System-Using-Machine-Learning
Awesome-Dataset-Distillation vs Intrusion-Detection-System-Using-Machine-Learning
MinVIS vs Cold-Diffusion-Models
Awesome-Dataset-Distillation vs VideoX
MinVIS vs bitsandbytes
Awesome-Dataset-Distillation vs PeRFception
MinVIS vs PeRFception
Awesome-Dataset-Distillation vs bitsandbytes
MinVIS vs textual_inversion
Awesome-Dataset-Distillation vs Cold-Diffusion-Models
MinVIS vs walk_in_the_park