Patrick Glandorf

Patrick Glandorf, Timo Kaiser, Bodo Rosenhahn

Sparse neural networks are a key factor in developing resource-efficient machine learning applications. We propose the novel and powerful sparse learning method Adaptive Regularized Training (ART) to compress dense into sparse networks. Instead of the commonly used binary mask during training to reduce the number of model weights, we inherently shrink weights close to zero in an iterative manner with increasing weight regularization. Our method compresses the pre-trained model knowledge into the weights of highest magnitude. Therefore, we introduce a novel regularization loss named HyperSparse that exploits the highest weights while conserving the ability of weight exploration. Extensive experiments on CIFAR and TinyImageNet show that our method leads to notable performance gains compared to other sparsification methods, especially in extremely high sparsity regimes up to 99.8 percent model sparsity. Additional investigations provide new insights into the patterns that are encoded in weights with high magnitudes.

Patrick Glandorf, Thomas Norrenbrock, Bodo Rosenhahn

Vision Transformers (ViTs) have demonstrated state-ofthe-art performance in several benchmarks, yet their high computational costs hinders their practical deployment. Patch Pruning offers significant savings, but existing approaches restrict token reduction to deeper layers, leaving early-stage compression unexplored. This limits their potential for holistic efficiency. In this work, we present a novel Video Patch Pruning framework (VPP) that integrates temporal prior knowledge to enable efficient sparsity within early ViT layers. Our approach is motivated by the observation that prior features extracted from deeper layers exhibit strong foreground selectivity. Therefore we propose a fully differentiable module for temporal mapping to accurately select the most relevant patches in early network stages. Notably, the proposed method enables a patch reduction of up to 60% in dense prediction tasks, exceeding the capabilities of conventional image-based patch pruning, which typically operate around a 30% patch sparsity. VPP excels the high-sparsity regime, sustaining remarkable performance even when patch usage is reduced below 55%. Specifically, it preserves stable results with a maximal performance drop of 0.6% on the Youtube-VIS 2021 dataset.

Patrick Glandorf, Bodo Rosenhahn

Vision Transformer have set new benchmarks in several tasks, but these models come with the lack of high computational costs which makes them impractical for resource limited hardware. Network pruning reduces the computational complexity by removing less important operations while maintaining performance. However, pruning a model on an unseen data domain, leads to a misevaluation of weight significance, resulting in suboptimal resource assignment. In this work, we find that task-sensitive layers initially fail to improve the feature representation on downstream tasks, leading to performance loss for early pruning decisions. To address this problem, we introduce Pruning by Block Benefit (P3B), a pruning method that utilizes the relative contribution on block level to globally assign parameter resources. P3B identifies low-impact components to reduce parameter allocation while preserving critical ones. Classical pruning mask optimization struggles to reactivate zero-mask-elements. In contrast, P3B sets a layerwise keep ratio based on global performance metrics, ensuring the reactivation of late-converging blocks. We show in extensive experiments that P3B is a state of the art pruning method with most noticeable gains in transfer learning tasks. Notably, P3B is able to conserve high performance, even in high sparsity regimes of 70% parameter reduction while only losing 0.64% in accuracy.