8 citations · 12 across the 12 of their papers we have counts for
12 papers
Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation
Yohann Benchetrit, Marlène Careil, Simon Dahan +3
Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate whether and when brain deco…
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
Hubert Banville, Stéphane d'Ascoli, Simon Dahan +12
Deep learning and large public datasets have recently catalyzed the proliferation of AI models for processing brain recordings. However, systematically evaluating these models rema…
NeuralSet: A High-Performing Python Package for Neuro-AI
Jean-Rémi King, Corentin Bel, Linnea Evanson +25
Artificial intelligence (AI) is increasingly central to understanding how the brain processes information. However, the integration of neuroscience and modern AI is bottlenecked by…
Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI
Marlène Careil, Yohann Benchetrit, Jean-Rémi King
Brain-to-image decoding has been recently propelled by the progress in generative AI models and the availability of large ultra-high field functional Magnetic Resonance Imaging (fM…
Consistency-diversity-realism Pareto fronts of conditional image generative models
Pietro Astolfi, Marlene Careil, Melissa Hall +5
Building world models that accurately and comprehensively represent the real world is the utmost aspiration for conditional image generative models as it would enable their use as…
PrivacyGAN: robust generative image privacy
Mariia Zameshina, Marlene Careil, Olivier Teytaud +1
Classical techniques for protecting facial image privacy typically fall into two categories: data-poisoning methods, exemplified by Fawkes, which introduce subtle perturbations to…
Towards image compression with perfect realism at ultra-low bitrates
Marlène Careil, Matthew J. Muckley, Jakob Verbeek +1
Image codecs are typically optimized to trade-off bitrate \vs distortion metrics. At low bitrates, this leads to compression artefacts which are easily perceptible, even when train…
Zero-shot spatial layout conditioning for text-to-image diffusion models
Guillaume Couairon, Marlène Careil, Matthieu Cord +2
Large-scale text-to-image diffusion models have significantly improved the state of the art in generative image modelling and allow for an intuitive and powerful user interface to…
Controllable Image Generation via Collage Representations
Arantxa Casanova, Marlène Careil, Adriana Romero-Soriano +3
Recent advances in conditional generative image models have enabled impressive results. On the one hand, text-based conditional models have achieved remarkable generation quality,…
Few-shot Semantic Image Synthesis with Class Affinity Transfer
Marlène Careil, Jakob Verbeek, Stéphane Lathuilière
Semantic image synthesis aims to generate photo realistic images given a semantic segmentation map. Despite much recent progress, training them still requires large datasets of ima…
Unifying conditional and unconditional semantic image synthesis with OCO-GAN
Marlène Careil, Stéphane Lathuilière, Camille Couprie +1
Generative image models have been extensively studied in recent years. In the unconditional setting, they model the marginal distribution from unlabelled images. To allow for more…
Instance-Conditioned GAN
Arantxa Casanova, Marlène Careil, Jakob Verbeek +2
Generative Adversarial Networks (GANs) can generate near photo realistic images in narrow domains such as human faces. Yet, modeling complex distributions of datasets such as Image…