Assessing and Explaining Deep Learning Model Compression

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description 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. In this context, we have proposed novel approaches for SSL model compression [15], particularly in the domains of speech recognition and emotion detection, achieving results that surpass the current state of the art. However, the increasing complexity of deep neural networks raises significant challenges in terms of interpretability and trust, especially in sensitive domains such as medicine [5] and automotive systems. Explainable Artificial Intelligence (XAI) addresses these concerns by making model decisions more transparent and understandable [6]. The THM team has made notable contributions to XAI research, particularly through the RisKa project, which applies these methods to ECG analysis [5], and more recently through the SIGN method, designed to reduce bias in model explanations [7]. This thesis