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Paul Albert
Paul Albert
Postdoctoral Researcher, Australian Insitute for Machine Learning
Verified email at adelaide.edu.au - Homepage
Title
Cited by
Cited by
Year
Pseudo-labeling and confirmation bias in deep semi-supervised learning
E Arazo, D Ortego, P Albert, NE O’Connor, K McGuinness
2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020
7942020
Unsupervised label noise modeling and loss correction
E Arazo, D Ortego, P Albert, N O’Connor, K McGuinness
International Conference on Machine Learning, 312-321, 2019
6072019
Multi-Objective Interpolation Training for Robustness to Label Noise
D Ortego, E Arazo, P Albert, NE O'Connor, K McGuinness
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
1012021
Towards robust learning with different label noise distributions
D Ortego, E Arazo, P Albert, NE O'Connor, K McGuinness
2020 25th International Conference on Pattern Recognition (ICPR), 7020-7027, 2021
312021
Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset
B Narayanan, M Saadeldin, P Albert, K McGuinness, B Mac Namee
arXiv preprint arXiv:2101.03198, 2021
152021
Addressing out-of-distribution label noise in webly-labelled data
P Albert, D Ortego, E Arazo, NE O'Connor, K McGuinness
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022
122022
Relab: Reliable label bootstrapping for semi-supervised learning
P Albert, D Ortego, E Arazo, N O'Connor, K McGuinness
2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021
92021
Semi-Supervised Dry Herbage Mass Estimation Using Automatic Data and Synthetic Images
P Albert, M Saadeldin, B Narayanan, B Mac Namee, D Hennessy, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
82021
How Important is Importance Sampling for Deep Budgeted Training?
E Arazo, D Ortego, P Albert, NE O'Connor, K McGuinness
arXiv preprint arXiv:2110.14283, 2021
72021
Embedding contrastive unsupervised features to cluster in-and out-of-distribution noise in corrupted image datasets
P Albert, E Arazo, NE O’Connor, K McGuinness
European Conference on Computer Vision, 402-419, 2022
62022
Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation
P Albert, M Saadeldin, B Narayanan, B Mac Namee, D Hennessy, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
62022
Is your noise correction noisy? PLS: Robustness to label noise with two stage detection
P Albert, E Arazo, T Krishna, NE O’Connor, K McGuinness
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023
32023
Using image analysis and machine learning to estimate sward clover content
D Hennessy, M Saad, B Mac Namee, NE O’Connor, K McGuinness, ...
European Grassland Federation Symposium, 2021
22021
Utilizing unsupervised learning to improve sward content prediction and herbage mass estimation
P Albert, M Saadeldin, B Narayanan, B Mac Namee, D Hennessy, ...
arXiv preprint arXiv:2204.09343, 2022
12022
Unifying Synergies between Self-supervised Learning and Dynamic Computation
T Krishna, AK Rai, A Drimbarean, E Arazo, P Albert, AF Smeaton, ...
arXiv preprint arXiv:2301.09164, 2023
2023
Deep learning for computer vision constrained by limited supervision
P Albert
Dublin City University, 2023
2023
Adaptation of Compositional Data Analysis in Deep Learning to Predict Pasture Biomass Proportions.
B Narayanan, M Saadeldin, P Albert, K McGuinness, NE O'Connor, ...
AICS, 176-187, 2021
2021
Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target
B Narayanan, M Saadeldin, P Albert, K McGuinness, B MacNamee
Narayanan, Badri, Mohamed Saadeldin, Paul Albert, Kevin McGuinness, and …, 2020
2020
Unsupervised label noise modeling and loss correction
E Arazo Sánchez, D Ortego, P Albert, NE O'Connor, K McGuinness
MIR Press, 2019
2019
Supplementary material for Addressing out-of-distribution label noise in webly-labelled data
P Albert, D Ortego, E Arazo, NE O’Connor, K McGuinness
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Articles 1–20