Lucas Relic — accepted AI-conference papers

2025

Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression

Lucas Relic, Roberto Azevedo, Yang Zhang, Markus Gross, Christopher Schroers

CVPR 2025poster

Generative neural image compression supports data representation with extremely low bitrate, allowing clients to synthesize details and consistently producing highly realistic images. By leveraging the similarities between quantization error and additive noise, diffusion-based generative image compr…

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2022

Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses

Jacob Granley, Lucas Relic, Michael Beyeler

NeurIPS 2022accept

Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capabilities. However, sensations elicited by current devices often appear artificial and distorted. Although current models can predict the neural or perceptual response to an electrical…

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