103 citations · 103 across the 8 of their papers we have counts for
10 papers
nnFoundation: 3D Foundation Models for Radiology
Constantin Ulrich Harsy, Tassilo Wald, Karol Gotkowski +80
Radiological artificial intelligence has advanced rapidly, yet most systems remain narrowly task-specific, data-intensive, and fragile under domain shift. Foundation models promise…
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
Moving by Looking: Towards Vision-Driven Avatar Motion Generation
Markos Diomataris, Berat Mert Albaba, Giorgio Becherini +3
The way we perceive the world fundamentally shapes how we move, whether it is how we navigate in a room or how we interact with other humans. Current human motion generation method…
RAVEN: Rethinking Adversarial Video Generation with Efficient Tri-plane Networks
Partha Ghosh, Soubhik Sanyal, Cordelia Schmid +1
We present a novel unconditional video generative model designed to address long-term spatial and temporal dependencies, with attention to computational and dataset efficiency. To…
Adversarial Likelihood Estimation With One-Way Flows
Omri Ben-Dov, Pravir Singh Gupta, Victoria Abrevaya +2
Generative Adversarial Networks (GANs) can produce high-quality samples, but do not provide an estimate of the probability density around the samples. However, it has been noted th…
Investigating self-supervised, weakly supervised and fully supervised training approaches for multi-domain automatic speech recognition: a study on Bangladeshi Bangla
Ahnaf Mozib Samin, M. Humayon Kobir, Md. Mushtaq Shahriyar Rafee +5
Despite huge improvements in automatic speech recognition (ASR) employing neural networks, ASR systems still suffer from a lack of robustness and generalizability issues due to dom…
LED: Latent Variable-based Estimation of Density
Omri Ben-Dov, Pravir Singh Gupta, Victoria Fernandez Abrevaya +2
Modern generative models are roughly divided into two main categories: (1) models that can produce high-quality random samples, but cannot estimate the exact density of new data po…
InvGAN: Invertible GANs
Partha Ghosh, Dominik Zietlow, Michael J. Black +2
Generation of photo-realistic images, semantic editing and representation learning are a few of many potential applications of high resolution generative models. Recent progress in…
From Variational to Deterministic Autoencoders
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari +2
Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models. However, learning a VAE from data poses still unanswered theoretica…
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders
Partha Ghosh, Arpan Losalka, Michael J Black
Susceptibility of deep neural networks to adversarial attacks poses a major theoretical and practical challenge. All efforts to harden classifiers against such attacks have seen li…