Paul Albert

Dr Paul Albert

Postdoctoral Researcher

Australian Institute for Machine Learning - Projects

Faculty of Sciences, Engineering and Technology

I am a postdoctoral researcher at the Australian Institute for Machine Learning, funded under the Center for Augmented Reasoning.

I am interested in weak, low and un- supervised problems for computer vision

  • Conference Papers
    Year Citation
    2023 Albert, P., Arazo, E., Krishna, T., O'Connor, N. E., & McGuinness, K. (2023). Is your noise correction noisy? PLS: Robustness to label noise with two stage detection. In Proceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023 (pp. 118-127). Online: IEEE.
    DOI Scopus5
    2022 Albert, P., Arazo, E., O’Connor, N. E., & McGuinness, K. (2022). Embedding Contrastive Unsupervised Features to Cluster In- And Out-of-Distribution Noise in Corrupted Image Datasets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13691 LNCS (pp. 402-419). Tel Aviv, Israel: Springer.
    DOI Scopus2
    2022 Albert, P., Saadeldin, M., Narayanan, B., Namee, B. M., Hennessy, D., O'Connor, N. E., & McGuinness, K. (2022). Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation. In Conference on Computer Vision and Pattern Recognition Workshops IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Vol. 2022-June (pp. 1635-1645). New Orleans, LA, USA: IEEE.
    DOI Scopus5
    2022 Albert, P., Ortego, D., Arazo, E., O'Connor, N. E., & McGuinness, K. (2022). Addressing out-of-distribution label noise in webly-labelled data. In Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022 (pp. 2393-2402). Waikola, HI, USA: IEEE.
    DOI Scopus9
    2021 Albert, P., Saadeldin, M., Narayanan, B., Namee, B. M., Hennessy, D., O'Connor, A., . . . McGuinness, K. (2021). Semi-supervised dry herbage mass estimation using automatic data and synthetic images. In Proceedings of the IEEE International Conference on Computer Vision Vol. 2021-October (pp. 1284-1293). Montreal, BC, Canada: IEEE.
    DOI Scopus4
    2021 Ortego, D., Arazo, E., Albert, P., O'Connor, N. E., & McGuinness, K. (2021). Multi-Objective Interpolation Training for Robustness to Label Noise. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 6602-6611). Nashville, TN, USA: IEEE.
    DOI Scopus64
    2021 Albert, P., Ortego, D., Arazo, E., O'Connor, N., & McGuinness, K. (2021). ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning. In Proceedings of the International Joint Conference on Neural Networks Vol. 2021-July (pp. 8 pages). Shenzhen, China: IEEE.
    DOI Scopus5
    2021 Ortego, D., Arazo, E., Albert, P., O'Connor, N. E., & McGuinness, K. (2021). Towards robust learning with different label noise distributions. In Proceedings - International Conference on Pattern Recognition (pp. 7020-7027). Milan, Italy: IEEE.
    DOI Scopus12
    2021 Narayanan, B., Saadeldin, M., Albert, P., McGuinness, K., O'Connor, N. E., & Namee, B. M. (2021). Adaptation of Compositional Data Analysis in Deep Learning to Predict Pasture Biomass Proportions. In CEUR Workshop Proceedings Vol. 3105 (pp. 176-187). Dublin: CEUR-WS.
    2021 Arazo, E., Ortego, D., Albert, P., O'Connor, N. E., & McGuinness, K. (2021). How Important is Importance Sampling for Deep Budgeted Training?. In 32nd British Machine Vision Conference, BMVC 2021. Virtual, Online: British Machine Vision Association, BMVA.
    Scopus1
    2020 Arazo, E., Ortego, D., Albert, P., O'Connor, N. E., & McGuinness, K. (2020). Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning. In Proceedings of the International Joint Conference on Neural Networks (pp. 8 pages). Glasgow, UK: IEEE.
    DOI Scopus509
    2019 Arazo, E., Ortego, D., Albert, P., O'Connor, N. E., & McGuinness, K. (2019). Unsupervised label noise modeling and loss correction. In K. Chaudhuri, & R. Salakhutdinov (Eds.), 36th International Conference on Machine Learning, ICML 2019 Vol. 2019-June (pp. 465-474). CA, Long Beach: JMLR-JOURNAL MACHINE LEARNING RESEARCH.
    Scopus181
  • Position: Postdoctoral Researcher
  • Phone: 3133380
  • Email: paul.albert@adelaide.edu.au
  • Campus: Lot 14
  • Building: Australian Institute for Machine Learning Building, floor Lower Ground
  • Room: LG.21
  • Org Unit: Australian Institute for Machine Learning - Projects
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External Profiles