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pro vyhledávání: '"Panagakis A"'
Recent advances in Diffusion Models (DMs) have led to significant progress in visual synthesis and editing tasks, establishing them as a strong competitor to Generative Adversarial Networks (GANs). However, the latent space of DMs is not as well unde
Externí odkaz:
http://arxiv.org/abs/2408.16845
Autor:
Mitofsky, Warren J.
Publikováno v:
The Public Opinion Quarterly, 1999 Jul 01. 63(2), 282-284.
Externí odkaz:
https://www.jstor.org/stable/2991261
This paper pertains to an emerging machine learning paradigm: learning higher-order functions, i.e. functions whose inputs are functions themselves, $\textit{particularly when these inputs are Neural Networks (NNs)}$. With the growing interest in arc
Externí odkaz:
http://arxiv.org/abs/2406.10685
Autor:
Koromilas, Panagiotis, Bouritsas, Giorgos, Giannakopoulos, Theodoros, Nicolaou, Mihalis, Panagakis, Yannis
What do different contrastive learning (CL) losses actually optimize for? Although multiple CL methods have demonstrated remarkable representation learning capabilities, the differences in their inner workings remain largely opaque. In this work, we
Externí odkaz:
http://arxiv.org/abs/2405.18045
Autor:
Melistas, Thomas, Spyrou, Nikos, Gkouti, Nefeli, Sanchez, Pedro, Vlontzos, Athanasios, Panagakis, Yannis, Papanastasiou, Giorgos, Tsaftaris, Sotirios A.
Publikováno v:
The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2024)
Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in domains such as natural image or medical imaging, modifications must r
Externí odkaz:
http://arxiv.org/abs/2403.20287
Autor:
Oldfield, James, Georgopoulos, Markos, Chrysos, Grigorios G., Tzelepis, Christos, Panagakis, Yannis, Nicolaou, Mihalis A., Deng, Jiankang, Patras, Ioannis
The Mixture of Experts (MoE) paradigm provides a powerful way to decompose dense layers into smaller, modular computations often more amenable to human interpretation, debugging, and editability. However, a major challenge lies in the computational c
Externí odkaz:
http://arxiv.org/abs/2402.12550
We present a locality-aware method for interpreting the latent space of wavelet-based Generative Adversarial Networks (GANs), that can well capture the large spatial and spectral variability that is characteristic to satellite imagery. By focusing on
Externí odkaz:
http://arxiv.org/abs/2309.14883
Autor:
Plitsis, Manos, Kouzelis, Theodoros, Paraskevopoulos, Georgios, Katsouros, Vassilis, Panagakis, Yannis
In this work, we investigate the personalization of text-to-music diffusion models in a few-shot setting. Motivated by recent advances in the computer vision domain, we are the first to explore the combination of pre-trained text-to-audio diffusers w
Externí odkaz:
http://arxiv.org/abs/2309.11140
Autor:
Hanks, Patrick, Lenarčič, Simon
Publikováno v:
Dictionary of American Family Names, 2 ed., 2022.
Externí odkaz:
http://www.oxfordreference.com/view/10.1093/acref/9780190245115.001.0001/acref-9780190245115-e-56143
Video-to-speech synthesis involves reconstructing the speech signal of a speaker from a silent video. The implicit assumption of this task is that the sound signal is either missing or contains a high amount of noise/corruption such that it is not us
Externí odkaz:
http://arxiv.org/abs/2307.16584