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Yamen Mubarka

3 accepted papers

2025

Leveraging Registers in Vision Transformers for Robust Adaptation

ICASSP 2025accepted

Vision Transformers (ViTs) have shown success across a variety of tasks due to their ability to capture global image representations. Recent studies have identified the existence of high-norm tokens in ViTs, which can interfere with unsupervised object discovery. To address this, the use of "registe…

Cited by 3SourceScholar
2024

On the Use of Anchoring for Training Vision Models

NeurIPS 2024spotlight

Anchoring is a recent, architecture-agnostic principle for training deep neural networks that has been shown to significantly improve uncertainty estimation, calibration, and extrapolation capabilities. In this paper, we systematically explore anchoring as a general protocol for training vision mode…

Cited by 0SourcePDFScholar
2022

Predicting the Generalization Gap in Deep Models using Anchoring

ICASSP 2022accepted

We address the problem of predicting the generalization gap of deep neural networks under large, natural, and synthetic distribution shifts between source and target domains. This is crucial in understanding how models behave in uncontrollable ‘in-the-wild’ scenarios, but existing techniques fail wh…

Cited by 0SourceScholar