244 citations · 355 across the 15 of their papers we have counts for
9 papers · 1 filter
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling
Etienne Bennequin, Myriam Tami, Antoine Toubhans +1
Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks. Nevertheless, we observe that these cur…
Optimizing Active Learning for Low Annotation Budgets
Umang Aggarwal, Adrian Popescu, Céline Hudelot
When we can not assume a large amount of annotated data , active learning is a good strategy. It consists in learning a model on a small amount of annotated data (annotation budget…
AVAE: Adversarial Variational Auto Encoder
Antoine Plumerault, Hervé Le Borgne, Céline Hudelot
Among the wide variety of image generative models, two models stand out: Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs can produce realistic image…
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification
Martin Charachon, Céline Hudelot, Paul-Henry Cournède +2
Explaining decisions of black-box classifiers is paramount in sensitive domains such as medical imaging since clinicians confidence is necessary for adoption. Various explanation a…
Spatial Contrastive Learning for Few-Shot Classification
Yassine Ouali, Céline Hudelot, Myriam Tami
In this paper, we explore contrastive learning for few-shot classification, in which we propose to use it as an additional auxiliary training objective acting as a data-dependent r…
Autoregressive Unsupervised Image Segmentation
Yassine Ouali, Céline Hudelot, Myriam Tami
In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs. Taking inspirati…