University of Warsaw

The work proposes GPart (Global Partition fine-tuning), a highly parameter-efficient fine-tuning method that maps a d-dimensional trainable vector directly into the full weight space through a sparse, isometric partition matrix, retains a fixed global parameter-sharing prior while removing the additional low-rank reconstruction used by LoRA-based methods, and thereby yields a single main hyperparameter (d), exact end-to-end isometry, and a minimal checkpoint representation consisting of the trainable vector and a random seed, matching or improving over existing PEFT methods at ultra-low parameter budgets across natural language understanding, computer vision, and mathematical reasoning benchmarks.

The work proposes GPart (Global Partition fine-tuning), a highly parameter-efficient fine-tuning method that maps a d-dimensional trainable vector directly into the full weight space through a sparse, isometric partition matrix, retains a fixed global parameter-sharing prior while removing the additional low-rank reconstruction used by LoRA-based methods, and thereby yields a single main hyperparameter (d), exact end-to-end isometry, and a minimal checkpoint representation consisting of the trainable vector and a random seed, matching or improving over existing PEFT methods at ultra-low parameter budgets across natural language understanding, computer vision, and mathematical reasoning benchmarks.

This study evaluated 11 vision models from the ResNet, DenseNet, and Vision Transformer families (seven trained from scratch, four pretrained) on the Oxford-IIIT Pet and Chest X-ray Pneumothorax datasets with ground-truth segmentation masks, generated explanations with five post-hoc XAI methods (Saliency, GradientSHAP, Integrated Gradients, Feature Permutation, Grad-CAM), and quantified mask alignment using Relevance Rank Accuracy and the proposed Dual-Polarity Precision; it found that increasing depth and parameter count did not improve explanation quality in most statistical comparisons, that smaller models often matched or exceeded deeper variants, that pretraining typically improved predictive performance and increased the dependence of explanations on learned weights without consisten

This study evaluated 11 vision models from the ResNet, DenseNet, and Vision Transformer families (seven trained from scratch, four pretrained) on the Oxford-IIIT Pet and Chest X-ray Pneumothorax datasets with ground-truth segmentation masks, generated explanations with five post-hoc XAI methods (Saliency, GradientSHAP, Integrated Gradients, Feature Permutation, Grad-CAM), and quantified mask alignment using Relevance Rank Accuracy and the proposed Dual-Polarity Precision; it found that increasing depth and parameter count did not improve explanation quality in most statistical comparisons, that smaller models often matched or exceeded deeper variants, that pretraining typically improved predictive performance and increased the dependence of explanations on learned weights without consisten