Dual User-Adaptation (DUA) framework

Source code for CVPR2020 paper "Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem."

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.

Copyright by Matthias De Lange.