Bonjour à tous·tes,
Vous aviez sollicité assez fortement une présentation concernant le parcours menant à l'obtention du diplôme qu'est l'habilitation à diriger des recherches.
J'en profite pour vous rappeler la question du wooclap de la plénière de décembre :
[cid:0f0c4c64-1791-47ed-ae76-7797d0fc46b8]
(et pour rappeler aussi qu'ingénieur est un titre, et que les baccalauréat, licence, master et doctorat sont des grades 😉).
On y parlera des attendus (flous) et donc de ce qu'il faut faire pour avoir un dossier "soutenable".
Pour trouver le créneau début juin qui conviendra au plus grand nombre des personnes intéressées (y compris aux HDR du labo qui voudront apporter leur contribution lors de cette rencontre), un formulaire est disponible ici : https://forms.office.com/e/dC7dV5RDG4
Bonne journée,
Théo
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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
* 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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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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Idir Benouaret
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