Hannah Benita Teufel — accepted AI-conference papers

2024

Quality-Diversity through AI Feedback

Herbie Bradley, Andrew Dai, Hannah Benita Teufel, Jenny Zhang, Koen Oostermeijer, Marco Bellagente +4

ICLR 2024poster

In many text-generation problems, users may prefer not only a single response, but a diverse range of high-quality outputs from which to choose. Quality-diversity (QD) search algorithms aim at such outcomes, by continually improving and diversifying a population of candidates. However, the applicabi…

Cited by 50 Source ↗PDF ↗Scholar ↗

2023

Holistic Evaluation of Text-to-Image Models

Tony Lee, Michihiro Yasunaga, Chenlin Meng, Yifan Mai, Joon Sung Park, Agrim Gupta +12

NeurIPS 2023spotlight

The stunning qualitative improvement of text-to-image models has led to their widespread attention and adoption. However, we lack a comprehensive quantitative understanding of their capabilities and risks. To fill this gap, we introduce a new benchmark, Holistic Evaluation of Text-to-Image Models (H…

Cited by 148 Source ↗PDF ↗code ↗Scholar ↗

2023

MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation

Marco Bellagente, Manuel Brack, Hannah Benita Teufel, Felix Friedrich, Björn Deiseroth, Constantin Eichenberg +8

NeurIPS 2023poster

The recent popularity of text-to-image diffusion models (DM) can largely be attributed to the intuitive interface they provide to users. The intended generation can be expressed in natural language, with the model producing faithful interpretations of text prompts. However, expressing complex or nua…

Cited by 23 Source ↗PDF ↗Scholar ↗