PhD position H/F - Assessing and Explaining Deep Learning Model Compression

Research Work Scientific Context The success of deep neural networks is often constrained by their reliance on large amounts of labeled data, which are both costly and time-consuming to obtain. Prior work has demonstrated that models can be compressed by up to 84% without any loss in performance [4]. At the same time, self-supervised learning (SSL) has emerged as a promising alternative, enabling models to learn from unlabeled data. Moreover, SSL models offer the advantage of being adaptable to multiple downstream tasks thereby reducing the cost associated with training models for auxiliary tasks.…