Lightweight models for ocular segmentation using the IPAD methodVidovič, Matevž (Avtor) Peer, Peter (Mentor) Vitek, Matej (Komentor) IPADneural network pruningconvolutional neural networksbiometricsIt is known that deep neural networks are significantly overparameterized in most cases. Pruning reduces the computational complexity of the network and can even enhance its performance. To address this, we develop a framework for iterative pruning of convolutional neural networks, designed to support a variety of architectures in a user-friendly manner. We apply this framework to U-Net and SegNet models, which were trained for sclera and vein segmentation tasks on ocular images. The models were subsequently pruned using a diverse set of kernel importance measures, including the recently introduced Iterative Pruning with Activation Deviation (IPAD). We analyze the effects of pruning on model performance and compare the success of different kernel importance measures. The results we collected indicate a substantial performance advantage for pruned models over compact models trained from scratch, especially on the more challenging task of vein segmentation. We were able to confirm the superiority of the IPAD method in scenarios where ample training data is available and appropriate steps with respect to hyperparameter optimization can be taken given the computational budget.20252025-07-03 14:10:03Diplomsko delo/naloga170320VisID: 38296COBISS_ID: 243038979sl