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
Deep Learning