All Outputs (3)

CAPE: Context-Aware Private Embeddings for Private Language Learning (2021)
Presentation / Conference Contribution
Plant, R., Gkatzia, D., & Giuffrida, V. (2021). CAPE: Context-Aware Private Embeddings for Private Language Learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (7970-7978)

Neural language models have contributed to state-of-the-art results in a number of downstream applications including sentiment analysis, intent classification and others. However, obtaining text representations or embeddings using these models risks... Read More about CAPE: Context-Aware Private Embeddings for Private Language Learning.

Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap (2021)
Journal Article
Litrico, M., Battiato, S., Tsaftaris, S. A., & Giuffrida, M. V. (2021). Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap. Journal of Imaging, 7(10), Article 198. https://doi.org/10.3390/jimaging7100198

This paper proposes a novel approach for semi-supervised domain adaptation for holistic regression tasks, where a DNN predicts a continuous value y∈R given an input image x. The current literature generally lacks specific domain adaptation approaches... Read More about Semi-Supervised Domain Adaptation for Holistic Counting under Label Gap.

Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses (2021)
Presentation / Conference Contribution
Frangulea, M., Pantos, C., Giuffrida, V., & Valente, J. (2021). Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses. In Precision agriculture ’21 (493-500). https://doi.org/10.3920/978-9

A tool for plant phenotyping is proposed to aid users in analyzing data on-demand. This tool is web-based and runs deep learning models. The current study focuses on the development of this tool, as well as obtaining a plant dataset to train a neural... Read More about Plant phenotyping on-demand: an integrative web-based framework using drones and participatory sensing in greenhouses.