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Wasserstein Barycenter Transport for Multi-Source Domain Adaptation

This repository contains the implementation of the so-called Wasserstein Barycenter Transport Algorithm, explored in the following publications,

Eduardo F. Montesuma, Fred-Maurice Ngolè Mboula (2021, June). Wasserstein Barycenter Transport for Multi-Source Domain Adaptation. In 2021 IEEE conference on computer vision and pattern recognition. [Paper] [Supplementary]

Eduardo F. Montesuma, Fred-Maurice Ngolè Mboula, "Wasserstein Barycenter Transport for Domain Adaptation", International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2021. [IEEE Explore]

News

12/06/2021: CVPR publication is available at the CVF repository.
27/05/2021: GPU implementation for the Wasserstein Barycenter Transport/Sinkhorn algorithm is now available using torch. This implementation is preferred as it speeds up computation.
13/05/2021: ICASSP publication is on IEEE Explore. Access is free for a month.

Intuition

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Modules

In this repo we provide a single package that implements all tested domain adaptation algorithms. Especially, TCA and KMM were implemented using the libtlda toolbox and OT-related methods were implemented through the POT toolbox. The implementations can be found in the ./msda folder.

Data

You can either use pre-extracted featuers (available on ./data folder) or download the samples and run the generation scripts provided in this repo.

Music-Speech Discrimination

Music Speech Recognition Source Direct Link
Noise Dataset Source

Music Genre Recognition

GTZAN Music Genre Recognition Source Direct Link
Noise Dataset Source

Face Recognition

Caltech-Office Decaf features Source Direct Link

Object Recognition

PIE Dataset Source

NOTE: on the ICASSP publication we explore solely Music-Speech Discrimination and Music-Genre Recognition. In the CVPR publication, we explore all four.

Results

Results for Music Genre Recognition (MGR)

Method	Buccaneer2	Destroyerengine	F16	Factory2
Baseline	22.90 ± 0.84	38.25 ± 0.91	51.57 ± 1.11	47.80 ± 0.34
KMM	21.75 ± 0.99	39.25 ± 0.66	49.81 ± 1.69	47.37 ± 0.71
TCA	<ins>58.95 ± 1.27</ins>	60.67 ± 2.07	<ins>68.75 ± 2.11</ins>	59.82 ± 0.50
SinT	56.35 ± 0.84	<ins>61.92 ± 1.64</ins>	66.72 ± 1.86	61.77 ± 1.65
SinT<sub>reg</sub>	58.02 ± 1.45	60.47 ± 1.75	66.55 ± 1.60	<ins>63.87 ± 1.51</ins>
JCPOT	35.87 ± 0.41	48.47 ± 2.97	51.92 ± 3.25	51.95 ± 1.75
JCPOT-LP	36.40 ± 0.39	52.92 ± 1.32	56.30 ± 0.37	51.52 ± 2.28
WBT	21.37 ± 2.25	24.30 ± 2.71	25.30 ± 6.02	22.70 ± 2.25
WBT<sub>reg</sub>	70.60 ± 1.33	83.10 ± 1.64	83.92 ± 1.01	90.00 ± 0.86
Target-only	67.43 ± 1.43	67.96 ± 2.91	66.86 ± 2.00	68.37 ± 1.87

Results for Music-Speech Discrimination (MSD)

Method	Buccaneer2	Destroyerengine	F16	Factory2
Baseline	82.43 ± 1.75	51.57 ± 2.56	88.89 ± 2.72	50.02 ± 2.21
KMM	87.12 ± 2.79	52.35 ± 2.94	74.86 ± 5.58	50.41 ± 2.17
TCA	90.43 ± 1.40	87.14 ± 4.99	95.12 ± 2.02	84.76 ± 3.30
SinT	89.26 ± 1.56	82.84 ± 2.78	84.97 ± 3.09	<ins>91.21 ± 2.04</ins>
SinT<sub>reg</sub>	87.28 ± 2.97	84.38 ± 1.76	86.14 ± 2.79	90.61 ± 1.68
JCPOT	<ins>92.55 ± 2.11</ins>	<ins>87.89 ± 1.39</ins>	88.67 ± 1.67	82.41 ± 2.22
JCPOT-LP	89.06 ± 1.38	84.97 ± 3.23	90.24 ± 1.71	86.13 ± 1.88
WBT	56.88 ± 9.54	56.63 ± 6.88	56.63 ± 6.56	59.38 ± 2.61
WBT<sub>reg</sub>	96.42 ± 1.48	92.79 ± 2.95	<ins>93.75 ± 0.97</ins>	95.31 ± 1.11
Target-only	90.51 ± 3.98	93.07 ± 3.81	89.23 ± 4.25	92.30 ± 3.62

Citation

If you find this work useful in your research, please consider citing us using the bibtex below,

CVPR

@InProceedings{montesuma2021cvpr,
    author    = {Montesuma, Eduardo Fernandes and Mboula, Fred Maurice Ngole},
    title     = {Wasserstein Barycenter for Multi-Source Domain Adaptation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {16785-16793}
}

ICASSP

@INPROCEEDINGS{montesuma2021icassp,
  author={Montesuma, Eduardo F. and Ngolè Mboula, Fred-Maurice},
  booktitle={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Wasserstein Barycenter Transport for Acoustic Adaptation}, 
  year={2021},
  volume={},
  number={},
  pages={3405-3409},
  doi={10.1109/ICASSP39728.2021.9414199}}