| 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 | 588 | 2020 |
| 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 | 507 | 2019 |
| 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 | 61 | 2021 |
| 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 | 26 | 2021 |
| 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 | 13 | 2021 |
| 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 | 9 | 2022 |
| 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 | 8 | 2021 |
| 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 | 5 | 2021 |
| 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 Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel …, 2022 | 3 | 2022 |
| 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 | 3 | 2022 |
| 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 | 3 | 2021 |
| 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 | 2 | 2021 |
| 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 | 1 | 2023 |
| 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 | | 2022 |
| 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 |
| 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 | | |
Kevin McGuinnessAssistant Professor, Dublin City UniversityDirección de correo verificada de dcu.ie
