8 citations · 20 across the 16 of their papers we have counts for
16 papers
DART: A Degradation-Aware Recurrent Transformer for Archival Film Restoration
Mikołaj Jastrzębski, Wojciech Kozłowski, Kamil Adamczewski
Archival film restoration is a challenging problem because historical footage contains compound degradations such as scratches, dust, blur, noise, flicker, and photometric aging, w…
AbsoluteDegradation: A Physics-Inspired Synthetic Film-Degradation Pipeline and Archival Film Restoration Benchmark
Mikołaj Jastrzębski, Dawid Glinkowski, Dawid Zieliński +3
Restoring archival film remains a fundamentally challenging problem due to the absence of paired training data and the lack of standardized evaluation benchmarks. Pristine versions…
Unifying Deep Stochastic Processes for Image Enhancement
Wojciech Kozłowski, Radosław Kuczbański, Kamil Adamczewski +2
Deep stochastic processes have recently become a central paradigm for image enhancement, with many methods explicitly conditioning the stochastic trajectory on the degraded input.…
Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models
Jakub Binkowski, Kamil Adamczewski, Tomasz Kajdanowicz
Large language models frequently exhibit hallucinations: fluent and confident outputs that are factually incorrect or unsupported by the input context. While recent hallucination d…
One Shot vs. Iterative: Rethinking Pruning Strategies for Model Compression
Mikołaj Janusz, Tomasz Wojnar, Yawei Li +2
Pruning is a core technique for compressing neural networks to improve computational efficiency. This process is typically approached in two ways: one-shot pruning, which involves…
Shapley Pruning for Neural Network Compression
Kamil Adamczewski, Yawei Li, Luc van Gool
Neural network pruning is a rich field with a variety of approaches. In this work, we propose to connect the existing pruning concepts such as leave-one-out pruning and oracle prun…