1 code implementation • 19 Apr 2024 • Mostafa ElAraby, Ali Harakeh, Liam Paull
Besides the common problem of classical catastrophic forgetting in the continual learning setting, CSS suffers from the inherent ambiguity of the background, a phenomenon we refer to as the "background shift'', since pixels labeled as background could correspond to future classes (forward background shift) or previous classes (backward background shift).
no code implementations • 4 Apr 2024 • Sabyasachi Sahoo, Mostafa ElAraby, Jonas Ngnawe, Yann Pequignot, Frederic Precioso, Christian Gagne
In this paper, we propose Layerwise EArly STopping (LEAST) for TTA to address this problem.
no code implementations • 22 Dec 2023 • Mostafa ElAraby, Sabyasachi Sahoo, Yann Pequignot, Paul Novello, Liam Paull
To build this space, GROOD relies on class prototypes together with a prototype that specifically captures OOD characteristics.
1 code implementation • 31 Mar 2020 • Victor Schmidt, Makesh Narsimhan Sreedhar, Mostafa ElAraby, Irina Rish
Unpaired Image-to-Image Translation (I2IT) tasks often suffer from lack of data, a problem which self-supervised learning (SSL) has recently been very popular and successful at tackling.
1 code implementation • 17 Feb 2020 • Mostafa ElAraby, Guy Wolf, Margarida Carvalho
We introduce a mixed integer program (MIP) for assigning importance scores to each neuron in deep neural network architectures which is guided by the impact of their simultaneous pruning on the main learning task of the network.
no code implementations • 26 Feb 2018 • Mostafa Elaraby, Ahmed Y. Tawfik, Mahmoud Khaled, Hany Hassan, Aly Osama
One of the challenges of SLT is the translation from a language without gender agreement to a language with gender agreement such as English to Arabic.
no code implementations • IWSLT 2017 • Hany Hassan, Mostafa ElAraby, Ahmed Tawfik
Our approach is language independent and can be used to generate data for any variant of the source language such as slang or spoken dialect or even for a different language that is closely related to the source language.