Selected Publications

Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization

We propose a new fine-tuning strategy that improves OOD generalization in computer vision by recycling and averaging weights specialized on diverse auxiliary tasks.

Diverse Weight Averaging for Out-of-Distribution Generalization

To improve out-of-distribution generalization on DomainBed, we average diverse weights obtained from different training runs; this strategy is motivated by an extension of the bias-variance theory to weight averaging.

DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion

We propose a new dynamic transformer architecture for continual learning with state-of-the-art performances.

Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization

We introduce and motivate a new regularization that enforces invariance in the domain-level gradient variances across the different training domains in order to improve out-of-distribution generalization.

MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks

We introduce a new generalized framework for learning multi-input multi-output subnetworks and study how to best mix the inputs. We obtain sota on CIFAR and Tiny ImageNet by better leveraging the expressiveness of large networks.

DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation

Driven by arguments from information theory, we introduce a new learning strategy for deep ensembles that increases diversity among members: we adversarially prevent features from being conditionally redundant.

CORE: Color Regression for Multiple Colors Fashion Garments

We detect continuous colors for fashion garments using a new architecture.

OMNIA Faster R-CNN: Detection in the Wild through Dataset Merging and Soft Distillation

We improve performances of object detectors via combining different datasets through soft distillation.

Leveraging Weakly Annotated Data for Fashion Image Retrieval and Label Prediction

We present a method to learn a visual representation adapted for e-commerce products.


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Deep Learning for Computer Vision
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