End-to-end Sinkhorn Autoencoder with Noise Generator. - Kamil Deja; Jan Dubinski; Piotr Nowak; Sandro Wenzel; Tomasz Trzcinski (CoRR, 2020)

On Robustness of Generative Representations Against Catastrophic Forgetting. - Wojciech Masarczyk; Kamil Deja; Tomasz Trzcinski (ICONIP (6), 2021)

BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning. - Kamil Deja; Pawel Wawrzynski; Daniel Marczak; Wojciech Masarczyk; Tomasz Trzcinski (IJCNN, 2021)

End-to-End Sinkhorn Autoencoder With Noise Generator. - Kamil Deja; Jan Dubinski; Piotr Nowak; Sandro Wenzel; Przemyslaw Spurek; Tomasz Trzcinski (IEEE Access, 2021)

Multiband VAE: Latent Space Partitioning for Knowledge Consolidation in Continual Learning. - Kamil Deja; Pawel Wawrzynski; Daniel Marczak; Wojciech Masarczyk; Tomasz Trzcinski (CoRR, 2021)

Selectively Increasing the Diversity of GAN-Generated Samples. - Jan Dubinski; Kamil Deja; Sandro Wenzel; Przemyslaw Rokita; Tomasz Trzcinski (ICONIP (1), 2022)

Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning. - Kamil Deja; Pawel Wawrzynski; Wojciech Masarczyk; Daniel Marczak; Tomasz Trzcinski (IJCAI, 2022)

Automatic Evaluation of Speaker Similarity. - Kamil Deja; Ariadna Sánchez; Julian Roth; Marius Cotescu (INTERSPEECH, 2022)

On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models. - Kamil Deja; Anna Kuzina; Tomasz Trzcinski; Jakub M. Tomczak (NeurIPS, 2022)

Logarithmic Continual Learning. - Wojciech Masarczyk; Pawel Wawrzynski; Daniel Marczak; Kamil Deja; Tomasz Trzcinski (IEEE Access, 2022)

Modelling Low-Resource Accents Without Accent-Specific TTS Frontend. - Georgi Tinchev; Marta Czarnowska; Kamil Deja; Kayoko Yanagisawa; Marius Cotescu (ICASSP, 2023)

Diffusion-based accent modelling in speech synthesis. - Kamil Deja; Georgi Tinchev; Marta Czarnowska; Marius Cotescu; Jasha Droppo (INTERSPEECH, 2023)

Learning Data Representations with Joint Diffusion Models. - Kamil Deja; Tomasz Trzcinski; Jakub M. Tomczak (ECML/PKDD (2), 2023)

Exploring Continual Learning of Diffusion Models. - Michal Zajac 005; Kamil Deja; Anna Kuzina; Jakub M. Tomczak; Tomasz Trzcinski; Florian Shkurti; Piotr Milos (CoRR, 2023)

Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN. - Jan Dubinski; Kamil Deja; Sandro Wenzel; Przemyslaw Rokita; Tomasz Trzcinski (CoRR, 2023)

Bayesian Flow Networks in Continual Learning. - Mateusz Pyla; Kamil Deja; Bartlomiej Twardowski; Tomasz Trzcinski (CoRR, 2023)

Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning. - Valeriya Khan; Sebastian Cygert; Kamil Deja; Tomasz Trzcinski; Bartlomiej Twardowski (IEEE Access, 2024)

Machine-learning-based particle identification with missing data. - Milosz Kasak; Kamil Deja; Maja Karwowska; Monika Jakubowska; Lukasz Graczykowski; Malgorzata Janik (CoRR, 2024)

GUIDE: Guidance-based Incremental Learning with Diffusion Models. - Bartosz Cywinski; Kamil Deja; Tomasz Trzcinski; Bartlomiej Twardowski; Lukasz Kucinski (CoRR, 2024)

Particle identification with machine learning from incomplete data in the ALICE experiment. - Maja Karwowska; Lukasz Graczykowski; Kamil Deja; Milosz Kasak; Malgorzata Janik (CoRR, 2024)

Particle physics DL-simulation with control over generated data properties. - Karol Rogozinski; Jan Dubinski; Przemyslaw Rokita; Kamil Deja (CoRR, 2024)

Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN. - Mikolaj Kita; Jan Dubinski; Przemyslaw Rokita; Kamil Deja (CoRR, 2024)

Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERN. - Patryk Bedkowski; Jan Dubinski; Kamil Deja; Przemyslaw Rokita (CoRR, 2024)

Low-Rank Continual Personalization of Diffusion Models. - Lukasz Staniszewski; Katarzyna Zaleska; Kamil Deja (CoRR, 2024)

There and Back Again: On the relation between noises, images, and their inversions in diffusion models. - Lukasz Staniszewski; Lukasz Kucinski; Kamil Deja (CoRR, 2024)

Joint Diffusion models in Continual Learning. - Pawel Skiers; Kamil Deja (CoRR, 2024)

Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation. - Joanna Kaleta; Pawel Skiers; Jan Dubinski; Przemyslaw Korzeniowski; Kamil Deja (CoRR, 2024)

Precise Parameter Localization for Textual Generation in Diffusion Models. - Lukasz Staniszewski; Bartosz Cywinski; Franziska Boenisch; Kamil Deja; Adam Dziedzic (ICLR, 2025)

Adapt & Align: Continual Learning with Generative Models' Latent Space Alignment. - Kamil Deja; Bartosz Cywinski; Jan Rybarczyk; Tomasz Trzcinski (Neurocomputing, 2025)

SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders. - Bartosz Cywinski; Kamil Deja (CoRR, 2025)