2 citations · 5 across the 14 of their papers we have counts for
6 papers · 1 filter
Privacy Attacks on Image AutoRegressive Models
Antoni Kowalczuk, Jan Dubiński, Franziska Boenisch +1
Image AutoRegressive generation has emerged as a new powerful paradigm with image autoregressive models (IARs) matching state-of-the-art diffusion models (DMs) in image quality (FI…
Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation
Joanna Kaleta, Paweł Skierś, Jan Dubiński +2
We introduce Mediffusion -- a new method for semi-supervised learning with explainable classification based on a joint diffusion model. The medical imaging domain faces unique chal…
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks
Antoni Kowalczuk, Jan Dubiński, Atiyeh Ashari Ghomi +6
Large-scale vision models have become integral in many applications due to their unprecedented performance and versatility across downstream tasks. However, the robustness of these…
Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN
Jan Dubiński, Kamil Deja, Sandro Wenzel +2
Currently, over half of the computing power at CERN GRID is used to run High Energy Physics simulations. The recent updates at the Large Hadron Collider (LHC) create the need for d…
Progressive Latent Replay for efficient Generative Rehearsal
Stanisław Pawlak, Filip Szatkowski, Michał Bortkiewicz +2
We introduce a new method for internal replay that modulates the frequency of rehearsal based on the depth of the network. While replay strategies mitigate the effects of catastrop…
Selectively increasing the diversity of GAN-generated samples
Jan Dubiński, Kamil Deja, Sandro Wenzel +2
Generative Adversarial Networks (GANs) are powerful models able to synthesize data samples closely resembling the distribution of real data, yet the diversity of those generated sa…