2 papers
cs.CV2026
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…
cs.CV2024
Understanding Transformer-based Vision Models through 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…