17 Apr
2024
17 Apr
'24
11:07 a.m.
Bonjour à tous,
Je vous annonce que le prochain séminaire de l'axe ML sera donné par Mariia Zameshina.
Je vous invite à consulter sa page web : https://sites.google.com/view/mzameshina . Mariia
a récemment réalisé sa thèse à l'Université Gustave Eiffel et Meta (Facebook AI
Research)
Ci-dessous, le titre et l'abstract du talk.
Merci de bien vouloir remplir le framadate suivant :
https://framadate.org/nprHBSOHNnZJeAiB
Au plus tard le vendredi 19 avril, afin de fixer rapidement un créneau pour le séminaire
Bonne fin de journée,
--------------------------------------------------------------------------------------------------------------------------------------------------------
Title: Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation
Abstract: Generative modeling methods can generate images from textual or visual inputs.
However, diversity in the generated images persists as a major challenge of the existing
approaches. We address this issue head-on and demonstrating that
* the diversity of a generated batch of images is intrinsically linked to the
diversity within the latent variables
* leveraging the geometry of the latent space, we can establish an effective metric
for quantifying diversity; and
* employing this insight allows one to achieve a significantly enhanced diversity in
image generation beyond the capabilities of traditional random independent sampling.
This advancement is consistent across a variety of generative models, including latent
diffusion models and GANs. Additionally, we have integrated our contributions into a
widely recognized tool for generative image modeling, ensuring that our improvements are
accessible to the broader community. As a result, this work not only presents a
methodological advancement in generative modeling but also significantly broadens the
scope of potential applications by enhancing the diversity of generated images
Short bio: Mariia Zameshina recently completed her PhD at University Gustave Eiffel and
Meta (Facebook AI Research). During her PhD, her main research focus was on ethical AI,
including improving the fairness and diversity of generative models and preserving privacy
using these models. Before that, she completed her master's degree at Grenoble INP,
where her thesis was on explainable learning. She has also completed internships in
computer vision at Google and Align Technology.
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