https://github.com/mattdl/dua
Source code "Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem." @ CVPR2020
https://github.com/mattdl/dua
cvpr2020 framework importance personalization privacy scalability security unsupervised-learning
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Source code "Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem." @ CVPR2020
- Host: GitHub
- URL: https://github.com/mattdl/dua
- Owner: Mattdl
- License: other
- Created: 2020-03-17T17:11:39.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T10:06:31.000Z (over 2 years ago)
- Last Synced: 2025-03-28T03:32:39.481Z (26 days ago)
- Topics: cvpr2020, framework, importance, personalization, privacy, scalability, security, unsupervised-learning
- Language: Python
- Homepage: http://openaccess.thecvf.com/content_CVPR_2020/html/De_Lange_Unsupervised_Model_Personalization_While_Preserving_Privacy_and_Scalability_An_Open_CVPR_2020_paper.html
- Size: 254 KB
- Stars: 12
- Watchers: 1
- Forks: 3
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Dual User-Adaptation (DUA) framework
Source code for CVPR2020 paper ["Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem."](http://openaccess.thecvf.com/content_CVPR_2020/html/De_Lange_Unsupervised_Model_Personalization_While_Preserving_Privacy_and_Scalability_An_Open_CVPR_2020_paper.html)
**In short:** Personalization of models to local user images is prone to three main problems: scalability towards thousands of users, retaining user-privacy, and labeling local user data. Our Dual User-Adaptation framework (DUA) unveils a novel perspective to tackle all of these practical concerns and enables personalization on both the server and local user edge-device.
The code simulates the server and users, and provides 3 benchmarks to evaluate the efficacy of our DUA framework.
**Keywords:** Model Personalization, User Adaptation, Continual Learning, Domain Adaptation, Privacy, Scalability, Unsupervised Learning
## Running the code
Always execute the scripts from within the *"exp/"* directory.
- *exp/demo_script.sh*: Run demo pipeline for MAS-RACL and FIM-IMM baseline.
- *config.init*: Adapt where to store your datasets, models and results to external paths.
- *requirements.txt*: Install the required packages for this code.
```
pip install -r requirements.txt
```
To reproduce the results from our paper:
- *exp/exps_Scenes.sh*: Setups to reproduce results for the MIT Indoor Scenes based dataset.
- *exp/exps_Numbers.sh*: Setups to reproduce results for the MNIST-SVHN based Numbers dataset.
## Reference Results
Results obtained in paper: average accuracy (forgetting).
1. RACL results (see *exp/exps_Scenes.sh* and *exp/exps_Numbers.sh* to replicate results)
| | Alexnet | | VGG11 | | MLP |
|----------|----------------|-----------------|----------------|-----------------|---------------|
| Method | Category Prior | Transform Prior | Category Prior | Transform Prior | Numbers |
| MAS-RACL | 66.97 (0.88) | 47.04 (-0.27) | 77.32 (0.77) | 53.59 (-0.14) | 84.01 (-0.22) |
| FIM-RACL | 67.20 (0.73) | 47.32 (-0.51) | 76.53 (0.68) | 53.73 (-0.13) | 87.83 (0.30) |
| MAS-IMM | 67.39 (0.73) | 46.51 (-0.14) | 76.77 (0.30) | 53.49 (-0.17) | 84.36 (-0.40) |
| FIM-IMM | 67.42 (0.23) | 46.68 (-0.35) | 76.29 (0.43) | 53.14 (0.07) | 87.68 (0.07) |
2. AdaBN/AdaBN-S results (see *exp/exps_Scenes.sh* to replicate results)
| | | CatPrior | | | TransPrior | | |
|---------------|--------------|---------------|---------------|---------------|---------------|--------------|--------------|
| | Method | BN | AdaBN | AdaBN-S | BN | AdaBN | AdaBN-S |
| User-Specific | MAS-RACL | 58.05 (2.74) | 58.30 (2.34) | 60.68 (2.67) | 30.14 (2.69) | 30.19 (2.50) | 32.82 (3.25) |
| | FIM-RACL | 59.58 (2.14) | 59.71 (1.61) | 62.43 (1.84) | 32.15 (1.53) | 32.04 (1.33) | 34.80 (2.13) |
| | Task Experts | 80.78 (5.61) | n/a | n/a | 68.22 (11.35) | n/a | n/a |
| User-Agnostic | MAS-IMM | 55.55 (2.69) | 55.89 (2.69) | 58.87 (2.81) | 29.36 (2.63) | 29.15 (2.45) | 31.73 (3.22) |
| | FIM-IMM | 61.50 (-0.03) | 61.35 (-0.46) | 63.99 (-0.16) | 32.08 (1.32) | 31.86 (1.21) | 34.48 (2.05) |
| | MAS | 65.58 (3.96) | 64.15 (4.04) | 67.10 (4.66) | 37.32 (2.64) | 35.64 (2.88) | 40.51 (2.69) |
| | EWC | 66.20 (2.88) | 64.03 (3.43) | 67.54 (3.90) | 37.16 (2.85) | 35.44 (3.12) | 40.05 (3.18) |
| | LWF | 70.76 (0.73) | 70.37 (0.43) | 72.73 (1.03) | 40.22 (0.43) | 39.51 (0.12) | 43.07 (0.52) |
| | Joint | 75.75 (n/a) | 72.13 (n/a) | 76.39 (n/a) | 46.53 (n/a) | 41.18 (n/a) | 48.50 (n/a) |
## Citing and License
Using this code for your research? Consider citing our work:
```
@InProceedings{Lange_2020_CVPR,
author = {Lange, Matthias De and Jia, Xu and Parisot, Sarah and Leonardis, Ales and Slabaugh, Gregory and Tuytelaars, Tinne},
title = {Unsupervised Model Personalization While Preserving Privacy and Scalability: An Open Problem},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
```
This source code is released under a Attribution-NonCommercial-ShareAlike 4.0 International
license, hence free to use for research purposes! Find out more about it in the [LICENSE file](LICENSE).
Copyright by Matthias De Lange.