Publications associées à Text-to-image model

Fashioning Creative Expertise with Generative AI: Graphical Interfaces for GAN-Based Design Space Exploration Better Support Ideation Than Text Prompts for Diffusion Models

Pierre Dillenbourg, Richard Lee Davis, Kevin Gonyop Kim, Thiemo Wambsganss, Wei Jiang

This paper investigates the potential impact of deep generative models on the work of creative professionals. We argue that current generative modeling tools lack critical features that would make them useful creativity support tools, and introduce our own ...

2024

Robust NAS under adversarial training: benchmark, theory, and beyond

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Yongtao Wu

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

2024

Efficient local linearity regularization to overcome catastrophic overfitting

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Elias Abad Rocamora

Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly w ...

2024

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Jan Sickmann Hesthaven, Federico Pichi

The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

San Diego2024

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro, Xiaolong Lu

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Romain Christophe Rémy Fleury, Janez Rus

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

2024

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell

Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...

2024

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Martin Nicolas Everaert

Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

BMVA2023

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Text-to-image model

Text-to-Microstructure Generation Using Generative Deep Learning

Fashioning Creative Expertise with Generative AI: Graphical Interfaces for GAN-Based Design Space Exploration Better Support Ideation Than Text Prompts for Diffusion Models

Robust NAS under adversarial training: benchmark, theory, and beyond

Efficient local linearity regularization to overcome catastrophic overfitting

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Text Representation Learning for Low Cost Natural Language Understanding

Graph Chatbot

Chattez avec Graph Search

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Robust NAS under adversarial training: benchmark, theory, and beyond

Efficient local linearity regularization to overcome catastrophic overfitting

Text Representation Learning for Low Cost Natural Language Understanding

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Fashioning Creative Expertise with Generative AI: Graphical Interfaces for GAN-Based Design Space Exploration Better Support Ideation Than Text Prompts for Diffusion Models

Text-to-Microstructure Generation Using Generative Deep Learning

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text