Computer Vision Lab
Computer Vision Lab
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Kamil Deja
Assistant Professor
Latest
Learning Data Representations with Joint Diffusion Models
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
Selectively increasing the diversity of GAN-generated samples
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning
On robustness of generative representations against catastrophic forgetting
End-to-End Sinkhorn Autoencoder With Noise Generator
BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning
Generative models for fast cluster simulations in the TPC for the ALICE experiment
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