Petit rappel, pour ceux qui seraient intéressé par le séminaire de Mariia (présentation du séminaire, titre et abstract. cf mail ci-dessus)
Je fixe la date du séminaire en début de semaine prochaine.
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.
Au plus tard le vendredi 19 avril, afin de fixer rapidement un créneau pour le séminaire
Bonne fin de journée,
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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
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the diversity of a generated batch of images is intrinsically linked to the diversity within the latent variables
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leveraging the geometry of the latent space, we can establish an effective metric for quantifying diversity; and
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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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Idir Benouaret
Enseignant-Chercheur
+33 4 28 29 37 63
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