no code implementations • 25 May 2024 • Farhad Pourpanah, Mahdiyar Molahasani, Milad Soltany, Michael Greenspan, Ali Etemad
We first theoretically establish a connection between domain shift and alignment of gradients in unsupervised federated learning and show that aligning the gradients at both client and server levels can facilitate the generalization of the model to new (target) domains.
1 code implementation • 26 Jun 2023 • Mahdiyar Molahasani, Ali Etemad, Michael Greenspan
A continual learning solution is proposed to address the out-of-distribution generalization problem for pedestrian detection.
no code implementations • 23 Jun 2023 • Mahdiyar Molahasani, Michael Greenspan, Ali Etemad
Next, we assert that by treating the learning of the Head and Tail as two separate and sequential steps, Continual Learning (CL) methods can effectively update the weights of the learner to learn the Tail without forgetting the Head.
1 code implementation • 21 Feb 2022 • Mohsen Zand, Haleh Damirchi, Andrew Farley, Mahdiyar Molahasani, Michael Greenspan, Ali Etemad
As the detection and localization tasks are well-correlated and can be jointly tackled, our model benefits from a multitask solution by learning multiscale representations of encoded crowd images, and subsequently fusing them.