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Dr Jo Plested
Associate Lecturer

Dr Jo Plested

UNSW Canberra
School of Engineering & IT

Jo Plested is an Associate Lecturer who has been researching deep learning for eight years. Her expertise is focused on transfer learning for small specialised datasets. For three years, Jo lectured in and produced all course material and assessments for the deep learning section of the graduate level course “Neural Networks, Deep Learning and Bio-inspired Computing” for up to 250 students at the Australian National University. Jo is also part of a group that developed a data science and artificial intelligence short course for Defence. She is currently developing a new deep learning course for the University of New South Wales Canberra. She has supervised over 20 students doing Honours, Masters and Chief of Defence Force (CDF) one and two semester projects. She has mentored many more coursework students in research undertaken as part of her course. Over 20 of these projects are published as high ranking international conference and journal papers.
E-mail
j.plested@adfa.edu.au
  • Journal articles | 2020
    Yao Y; Plested J; Gedeon T, 2020, 'Information-preserving feature filter for short-term EEG signals', Neurocomputing, 408, pp. 91 - 99, http://dx.doi.org/10.1016/j.neucom.2019.11.106
  • Conference Papers | 2022
    2022, 'Deep learning based detection of high similarity objects on limited hardware robots', in Ishibuchi H; Kwoh C-K (ed.), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI) December 4 – 7, 2022, Singapore, IEEE SSCI, Singapore, pp. 1770 - 1771, presented at 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI) December 4 – 7, 2022, Singapore, Singapore, 04 December 2022 - 07 December 2022, https://rpsonline.com.sg/rps2prod/ieee-ssci2022/pdf/IEEE-SSCI22-341.pdf
    Conference Papers | 2022
    2022, 'Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies', in Ishibuchi H; Kwoh C-K (ed.), Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI) December 4 – 7, 2022, Singapore, IEEE SSCI, Singapore, pp. 1772 - 1773, presented at 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI) December 4 – 7, 2022, Singapore, Singapore, 04 December 2022 - 07 December 2022, https://rpsonline.com.sg/rps2prod/ieee-ssci2022/pdf/IEEE-SSCI22-344.pdf
    Conference Papers | 2022
    2022, 'FERM: A FEature-space Representation Measure for Improved Model Evaluation', in CEUR Workshop Proceedings
    Preprints | 2022
    2022, Deep transfer learning for image classification: a survey, , http://arxiv.org/abs/2205.09904v1
    Conference Papers | 2021
    2021, 'Exploring Biases and Prejudice of Facial Synthesis via Semantic Latent Space', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN52387.2021.9534287
    Conference Papers | 2021
    2021, 'Rethinking Binary Hyperparameters for Deep Transfer Learning', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 463 - 475, http://dx.doi.org/10.1007/978-3-030-92270-2_40
    Preprints | 2021
    2021, Non-binary deep transfer learning for image classification, , http://arxiv.org/abs/2107.08585v2
    Conference Papers | 2020
    Shen X; Plested J; Yao Y; Gedeon T, 2020, 'Pairwise-GAN: Pose-Based View Synthesis Through Pair-Wise Training', in Communications in Computer and Information Science, pp. 507 - 515, http://dx.doi.org/10.1007/978-3-030-63820-7_58
    Conference Papers | 2020
    Wang Y; Plested J; Gedeon T, 2020, 'MultiTune: Adaptive Integration of Multiple Fine-Tuning Models for Image Classification', in Communications in Computer and Information Science, pp. 488 - 496, http://dx.doi.org/10.1007/978-3-030-63820-7_56
    Conference Papers | 2020
    2020, 'Declarative Residual Network for Robust Facial Expression Recognition', in Communications in Computer and Information Science, pp. 345 - 352, http://dx.doi.org/10.1007/978-3-030-63820-7_39
    Conference Papers | 2020
    2020, 'Exploring the Correlation between Random Convolutional Architectures and the Trained Equivalent', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN48605.2020.9207465
    Conference Papers | 2019
    Liu Y; Yao Y; Wang Z; Plested J; Gedeon T, 2019, 'Generalized Alignment for Multimodal Physiological Signal Learning', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2019.8852216
    Conference Papers | 2019
    Plested J; Gedeon T, 2019, 'An analysis of the interaction between transfer learning protocols in deep neural networks', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 312 - 323, http://dx.doi.org/10.1007/978-3-030-36708-4_26
    Conference Papers | 2019
    Yao Y; Plested J; Gedeon T; Liu Y; Wang Z, 2019, 'Improved Techniques for Building EEG Feature Filters', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN.2019.8852302
    Conference Papers | 2019
    2019, 'Evaluation on Neural Network Models for Video-Based Stress Recognition', in Communications in Computer and Information Science, pp. 440 - 447, http://dx.doi.org/10.1007/978-3-030-36802-9_47
    Conference Papers | 2019
    2019, 'Pruning Convolutional Neural Network with Distinctiveness Approach', in Communications in Computer and Information Science, pp. 448 - 455, http://dx.doi.org/10.1007/978-3-030-36802-9_48
    Conference Papers | 2019
    2019, 'Using an evolutionary algorithm to optimize the hyper-parameters of a cascading neural network', in Communications in Computer and Information Science, pp. 758 - 765, http://dx.doi.org/10.1007/978-3-030-36808-1_83
    Conference Papers | 2019
    2019, 'Using evolutionary algorithms for hyperparameter tuning and network reduction techniques to classify core porosity classes based on petrographical descriptions', in Communications in Computer and Information Science, pp. 750 - 757, http://dx.doi.org/10.1007/978-3-030-36808-1_82
    Conference Papers | 2018
    Yao Y; Plested J; Gedeon T, 2018, 'A Feature Filter for EEG Using Cycle-GAN Structure', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 567 - 576, http://dx.doi.org/10.1007/978-3-030-04239-4_51
    Conference Papers | 2018
    Yao Y; Plested J; Gedeon T, 2018, 'Deep Feature Learning and Visualization for EEG Recording Using Autoencoders', in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 554 - 566, http://dx.doi.org/10.1007/978-3-030-04239-4_50
    Conference Papers | 2018
    2018, 'Detection of universal cross-cultural depression indicators from the physiological signals of observers', in 2017 7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2017, pp. 185 - 192, http://dx.doi.org/10.1109/ACIIW.2017.8272612

Jo Plested has received over $270,000 in research grants, mostly in deep learning.  She has been lead investigator for $160,000 of those grants.