10 citations · 25 across the 20 of their papers we have counts for
6 papers · 1 filter
DREAM: Deep-Reparametrization of Adaptive Regularization Maps for Fast Zero-Shot Self-Supervised Learning
Thanh Trung Vu, Ander Biguri, Christoph Kolbitsch +3
Adaptive regularization is an effective means of improving the flexibility of classical variational reconstruction methods while retaining their interpretability and mathematical s…
Learning spatially varying regularisation parameters of low regularity for image reconstruction
Kostas Papafitsoros, Luca Calatroni, Andreas Kofler
In this chapter, we review and discuss the regularity properties of spatially adaptive regularisation weight functions used in variational image reconstruction. Incorporating such…
Centering Ecological Goals in Automated Identification of Individual Animals
Lukas Picek, Timm Haucke, Lukáš Adam +16
Recognizing individual animals over time is central to many ecological and conservation questions, including estimating abundance, survival, movement, and social structure. Recent…
Degradation-based augmented training for robust individual animal re-identification
Thanos Polychronou, Lukáš Adam, Viktor Penchev +1
Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based on their fine-scale unique morpho…
Split, Skip and Play: Variance-Reduced ProxSkip for Tomography Reconstruction is Extremely Fast
Evangelos Papoutsellis, Zeljko Kereta, Kostas Papafitsoros
Many modern iterative solvers for large-scale tomographic reconstruction incur two major computational costs per iteration: expensive forward/adjoint projections to update the data…
Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries
Joshua Schulz, David Schote, Christoph Kolbitsch +2
State-of-the-art learned reconstruction methods often rely on black-box modules that, despite their strong performance, raise questions about their interpretability and robustness.…