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Improving Personalisation in Valence and Arousal Prediction using Data Augmentation
This paper presents our work on an enhanced personalisation strategy, that leverages data augmentation to develop tailored models for continuous valence and arousal prediction.
Explainability Matters: Backdoor Attacks on Medical Imaging
Extensive evaluation of a state-of-the-art architecture demonstrates that by introducing images with few-pixel perturbations into the training set, an attacker can execute the backdoor successfully without having to be involved with the training procedure.
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