9 citations · 14 across the 15 of their papers we have counts for
6 papers · 1 filter
Multi-marginal temporal Schrödinger Bridge Matching from unpaired data
Thomas Gravier, Thomas Boyer, Auguste Genovesio
Many natural dynamic processes -- such as in vivo cellular differentiation or disease progression -- can only be observed through the lens of static sample snapshots. While challen…
The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
Qiuyu Chen, Xin Jin, Yue Song +45
This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…
DiViD: Disentangled Video Diffusion for Static-Dynamic Factorization
Marzieh Gheisari, Auguste Genovesio
Unsupervised disentanglement of static appearance and dynamic motion in video remains a fundamental challenge, often hindered by information leakage and blurry reconstructions in e…
A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features
Ihab Bendidi, Yassir El Mesbahi, Alisandra K. Denton +4
Understanding cellular responses to stimuli is crucial for biological discovery and drug development. Transcriptomics provides interpretable, gene-level insights, while microscopy…
DiffEx: Explaining a Classifier with Diffusion Models to Identify Microscopic Cellular Variations
Anis Bourou, Saranga Kingkor Mahanta, Thomas Boyer +2
In recent years, deep learning models have been extensively applied to biological data across various modalities. Discriminative deep learning models have excelled at classifying i…
Revealing Subtle Phenotypes in Small Microscopy Datasets Using Latent Diffusion Models
Anis Bourou, Biel Castaño Segade, Thomas Boyer +2
Identifying subtle phenotypic variations in cellular images is critical for advancing biological research and accelerating drug discovery. These variations are often masked by the…