1 citations · 1 across the 3 of their papers we have counts for
3 papers
ProtoQuant: Quantization of Prototypical Parts For General and Fine-Grained Image Classification
Mikołaj Janusz, Adam Wróbel, Bartosz Zieliński +1
Prototypical parts-based models offer a "this looks like that" paradigm for intrinsic interpretability, yet they typically struggle with ImageNet-scale generalization and often req…
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…
OMENN: One Matrix to Explain Neural Networks
Adam Wróbel, Mikołaj Janusz, Bartosz Zieliński +1
Deep Learning (DL) models are often black boxes, making their decision-making processes difficult to interpret. This lack of transparency has driven advancements in eXplainable Art…