176 citations · 538 across the 62 of their papers we have counts for
6 papers · 1 filter
OCR-VQGAN: Taming Text-within-Image Generation
Juan A. Rodriguez, David Vazquez, Issam Laradji +2
Synthetic image generation has recently experienced significant improvements in domains such as natural image or art generation. However, the problem of figure and diagram generati…
Constraining Representations Yields Models That Know What They Don't Know
Joao Monteiro, Pau Rodriguez, Pierre-Andre Noel +2
A well-known failure mode of neural networks is that they may confidently return erroneous predictions. Such unsafe behaviour is particularly frequent when the use case slightly di…
Workflow Discovery from Dialogues in the Low Data Regime
Amine El Hattami, Stefania Raimondo, Issam Laradji +3
Text-based dialogues are now widely used to solve real-world problems. In cases where solution strategies are already known, they can sometimes be codified into workflows and used…
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Gaurav Sahu, Pau Rodriguez, Issam H. Laradji +3
Data augmentation is a widely employed technique to alleviate the problem of data scarcity. In this work, we propose a prompting-based approach to generate labelled training data f…
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…
Overcoming challenges in leveraging GANs for few-shot data augmentation
Christopher Beckham, Issam Laradji, Pau Rodriguez +3
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. We perform an exploration into how a GAN can b…