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cs.CV2024
Parameter-Efficient Interventions for Enhanced Model Merging
Marcin Osial, Daniel Marczak, Bartosz Zieliński
Model merging combines knowledge from task-specific models into a unified multi-task model to avoid joint training on all task data. However, current methods face challenges due to…
cs.CV2024
Beyond [cls]: Exploring the true potential of Masked Image Modeling representations
Marcin Przewięźlikowski, Randall Balestriero, Wojciech Jasiński +2
Masked Image Modeling (MIM) has emerged as a promising approach for Self-Supervised Learning (SSL) of visual representations. However, the out-of-the-box performance of MIMs is typ…
cs.CV2024
Revisiting FunnyBirds evaluation framework for prototypical parts networks
Szymon Opłatek, Dawid Rymarczyk, Bartosz Zieliński
Prototypical parts networks, such as ProtoPNet, became popular due to their potential to produce more genuine explanations than post-hoc methods. However, for a long time, this pot…