no code implementations • 9 Mar 2024 • Arash Afkanpour, Vahid Reza Khazaie, Sana Ayromlou, Fereshteh Forghani
By directly conditioning generative models on a source image representation, our method enables the generation of diverse augmentations while maintaining the semantics of the source image, thus offering a richer set of data for self-supervised learning.
1 code implementation • 28 Sep 2023 • Fatemeh Tavakoli, D. B. Emerson, Sana Ayromlou, John Jewell, Amrit Krishnan, Yuchong Zhang, Amol Verma, Fahad Razak
Federated learning (FL) is increasingly being recognized as a key approach to overcoming the data silos that so frequently obstruct the training and deployment of machine-learning models in clinical settings.
1 code implementation • 26 Jun 2022 • Sana Ayromlou, Purang Abolmaesumi, Teresa Tsang, Xiaoxiao Li
Here, we propose a novel data-free class incremental learning framework that first synthesizes data from the model trained on previous classes to generate a \ours.
1 code implementation • 7 Oct 2021 • Mohammadhossein Bahari, Vahid Zehtab, Sadegh Khorasani, Sana Ayromlou, Saeed Saadatnejad, Alexandre Alahi
Finally, we illustrate how, by using SVG, one can benefit from datasets and advancements in other research fronts that also utilize the same input format.
no code implementations • 18 Mar 2021 • Reza Shirkavand, Sana Ayromlou, Soroush Farghadani, Maedeh-sadat Tahaei, Fattane Pourakpour, Bahareh Siahlou, Zeynab Khodakarami, Mohammad H. Rohban, Mansoor Fatehi, Hamid R. Rabiee
Fazekas scale facilitates an accurate quantitative assessment of the severity of white matter lesions and hence the disease.