Paul Albert

Orcid: 0000-0001-8220-272X

According to our database¹, Paul Albert authored at least 18 papers between 1999 and 2023.

Collaborative distances:

Dijkstra number² of five.
Erdős number³ of four.

Timeline

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PhD thesis

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On csauthors.net:

Bibliography

2023

Is your noise correction noisy? PLS: Robustness to label noise with two stage detection.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Unifying Synergies between Self-supervised Learning and Dynamic Computation.

[BibT_eX]

[DOI]

Proceedings of the 34th British Machine Vision Conference 2023, 2023

2022

Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation.

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[DOI]

CoRR, 2022

Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation.

[BibT_eX]

[DOI]

CoRR, 2022

Addressing out-of-distribution label noise in webly-labelled data.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Embedding Contrastive Unsupervised Features to Cluster In- And Out-of-Distribution Noise in Corrupted Image Datasets.

[BibT_eX]

[DOI]

Proceedings of the Computer Vision - ECCV 2022, 2022

Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022

2021

Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset.

[BibT_eX]

[DOI]

CoRR, 2021

ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

Semi-supervised dry herbage mass estimation using automatic data and synthetic images.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

Multi-Objective Interpolation Training for Robustness To Label Noise.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

How Important is Importance Sampling for Deep Budgeted Training?

[BibT_eX]

[DOI]

Proceedings of the 32nd British Machine Vision Conference 2021, 2021

Adaptation of Compositional Data Analysis in Deep Learning to Predict Pasture Biomass Proportions.

[BibT_eX]

[DOI]

Proceedings of the 29th Irish Conference on Artificial Intelligence and Cognitive Science 2021, 2021

2020

Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning.

[BibT_eX]

[DOI]

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Towards Robust Learning with Different Label Noise Distributions.

[BibT_eX]

[DOI]

Proceedings of the 25th International Conference on Pattern Recognition, 2020

2019

Unsupervised Label Noise Modeling and Loss Correction.

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[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

2012

Research discovery through linked open data.

[BibT_eX]

[DOI]

Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, 2012

1999

The efficacy of matching information systems development methodologies with application characteristics - an empirical study.

[BibT_eX]

[DOI]

J. Syst. Softw., 1999

Paul Albert

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