Швай, НадіяСеменець, Дарина2025-09-032025-09-032025https://ekmair.ukma.edu.ua/handle/123456789/36412In this study, the hypothesis is examined whether Dropout masks can be used for structural pruning without further evaluating the importance of individual filters or weight. It was decided to compare a Dropout-based approach, which is based on the utilization of binary Dropout masks, with a classical L2-Norm-based pruning method. For this task, we manually designed an architecture of a convolutional neural network with a custom Dropout. The research undergoes the following phases: designing a mask generation mechanism, preprocessing data, training of model, implementing of pruning algorithms, and conducting experiments using the Imagenette2 dataset. Our idea is to determine whether Dropout pruning can offer a reliable alternative to traditional methods, especially under different levels of sparsity and stochasticity.en-USPruningDropoutL2Convolutional Neural NetworkAlgorithmTrainingBinary MaskSeedbachelor`s thesisOther