no code implementations • CVPR 2023 • Islam Nassar, Munawar Hayat, Ehsan Abbasnejad, Hamid Rezatofighi, Gholamreza Haffari
Finally, ProtoCon addresses the poor training signal in the initial phase of training (due to fewer confident predictions) by introducing an auxiliary self-supervised loss.
1 code implementation • 19 Oct 2022 • Islam Nassar, Munawar Hayat, Ehsan Abbasnejad, Hamid Rezatofighi, Mehrtash Harandi, Gholamreza Haffari
We present LAVA, a simple yet effective method for multi-domain visual transfer learning with limited data.
no code implementations • 29 Sep 2021 • Xuanli He, Islam Nassar, Jamie Ryan Kiros, Gholamreza Haffari, Mohammad Norouzi
To obtain strong task-specific generative models, we either fine-tune a large language model (LLM) on inputs from specific tasks, or prompt a LLM with a few input examples to generate more unlabeled examples.
1 code implementation • 11 Jun 2021 • Xuanli He, Islam Nassar, Jamie Kiros, Gholamreza Haffari, Mohammad Norouzi
This paper studies the use of language models as a source of synthetic unlabeled text for NLP.
1 code implementation • CVPR 2021 • Islam Nassar, Samitha Herath, Ehsan Abbasnejad, Wray Buntine, Gholamreza Haffari
We train two classifiers with two different views of the class labels: one classifier uses the one-hot view of the labels and disregards any potential similarity among the classes, while the other uses a distributed view of the labels and groups potentially similar classes together.