5 citations · 8 across the 14 of their papers we have counts for
3 papers · 1 filter
Is Hierarchical Quantization Essential for Optimal Reconstruction?
Shirin Reyhanian, Laurenz Wiskott
Vector-quantized variational autoencoders (VQ-VAEs) are central to models that rely on high reconstruction fidelity, from neural compression to generative pipelines. Hierarchical e…
Understanding Transformer-Based Vision Models via Modular Feature Inversion
Jan Rathjens, Shirin Reyhanian, David Kappel +1
Understanding the internal mechanisms of deep neural networks remains a central challenge in machine learning. In computer vision, one promising yet only preliminarily explored app…
ProtoP-OD: Explainable Object Detection with Prototypical Parts
Pavlos Rath-Manakidis, Frederik Strothmann, Tobias Glasmachers +1
Interpretation and visualization of the behavior of detection transformers tends to highlight the locations in the image that the model attends to, but it provides limited insight…