Search Results for author: Isabelle Bloch

Found 20 papers, 4 papers with code

Template-Based Graph Clustering

1 code implementation5 Jul 2021 Mateus Riva, Florian Yger, Pietro Gori, Roberto M. Cesar Jr., Isabelle Bloch

We propose a novel graph clustering method guided by additional information on the underlying structure of the clusters (or communities).

Graph Clustering

Knowledge distillation from multi-modal to mono-modal segmentation networks

no code implementations17 Jun 2021 Minhao Hu, Matthis Maillard, Ya zhang, Tommaso Ciceri, Giammarco La Barbera, Isabelle Bloch, Pietro Gori

In this paper, we propose KD-Net, a framework to transfer knowledge from a trained multi-modal network (teacher) to a mono-modal one (student).

Brain Tumor Segmentation Knowledge Distillation +1

Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example

no code implementations12 May 2021 Robin Kips, Ruowei Jiang, Sileye Ba, Edmund Phung, Parham Aarabi, Pietro Gori, Matthieu Perrot, Isabelle Bloch

While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task.

Virtual Try-on

Approximation of dilation-based spatial relations to add structural constraints in neural networks

no code implementations22 Feb 2021 Mateus Riva, Pietro Gori, Florian Yger, Roberto Cesar, Isabelle Bloch

Several relations can be modeled as a morphological dilation of a reference object with a structuring element representing the semantics of the relation, from which the degree of satisfaction of the relation between another object and the reference object can be derived.

Object Recognition

Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification

1 code implementation4 Dec 2020 Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Severine Dubuisson, Isabelle Bloch

Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks.

Bayesian Inference Decision Making Under Uncertainty +4

Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations

no code implementations29 Sep 2020 Antoine Pirovano, Hippolyte Heuberger, Sylvain Berlemont, Saïd Ladjal, Isabelle Bloch

We formalize the design of WSI classification architectures and propose a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context.

Classification General Classification +2

CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer

no code implementations24 Aug 2020 Robin Kips, Pietro Gori, Matthieu Perrot, Isabelle Bloch

While existing makeup style transfer models perform an image synthesis whose results cannot be explicitly controlled, the ability to modify makeup color continuously is a desirable property for virtual try-on applications.

Image Generation Style Transfer +1

One Versus all for deep Neural Network Incertitude (OVNNI) quantification

no code implementations1 Jun 2020 Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Severine Dubuisson, Isabelle Bloch

This is due to the fact that modern DNNs are usually uncalibrated and we cannot characterize their epistemic uncertainty.

Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques

no code implementations20 Mar 2020 Qinkai Zheng, Han Qiu, Gerard Memmi, Isabelle Bloch

This report is about applications based on spatial-frequency transform and deep learning techniques.


TRADI: Tracking deep neural network weight distributions for uncertainty estimation

no code implementations ECCV 2020 Gianni Franchi, Andrei Bursuc, Emanuel Aldea, Severine Dubuisson, Isabelle Bloch

During training, the weights of a Deep Neural Network (DNN) are optimized from a random initialization towards a nearly optimum value minimizing a loss function.

General Classification Out-of-Distribution Detection +1

Max-plus Operators Applied to Filter Selection and Model Pruning in Neural Networks

1 code implementation19 Mar 2019 Yunxiang Zhang, Samy Blusseau, Santiago Velasco-Forero, Isabelle Bloch, Jesus Angulo

Following recent advances in morphological neural networks, we propose to study in more depth how Max-plus operators can be exploited to define morphological units and how they behave when incorporated in layers of conventional neural networks.

Explanatory relations in arbitrary logics based on satisfaction systems, cutting and retraction

no code implementations5 Mar 2018 Marc Aiguier, Jamal Atif, Isabelle Bloch, Ramón Pino-Pérez

The aim of this paper is to introduce a new framework for defining abductive reasoning operators based on a notion of retraction in arbitrary logics defined as satisfaction systems.

Morphologic for knowledge dynamics: revision, fusion, abduction

no code implementations14 Feb 2018 Isabelle Bloch, Jérôme Lang, Ramón Pino Pérez, Carlos Uzcátegui

Several tasks in artificial intelligence require to be able to find models about knowledge dynamics.

Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection

no code implementations16 Jan 2017 Hadrien Bertrand, Matthieu Perrot, Roberto Ardon, Isabelle Bloch

Improving the model is not an easy task, due to the large number of hyper-parameters governing both the architecture and the training of the network, and to the limited understanding of their relevance.

Gaussian Processes General Classification +1

Exploring Structure for Long-Term Tracking of Multiple Objects in Sports Videos

1 code implementation19 Dec 2016 Henrique Morimitsu, Isabelle Bloch, Roberto M. Cesar-Jr

In this paper, we propose a novel approach for exploiting structural relations to track multiple objects that may undergo long-term occlusion and abrupt motion.

Relaxation-based revision operators in description logics

no code implementations26 Feb 2015 Marc Aiguier, Jamal Atif, Isabelle Bloch, Céline Hudelot

In this paper we address both the generalization of the well-known AGM postulates, and the definition of concrete and well-founded revision operators in different DL families.

Belief Revision, Minimal Change and Relaxation: A General Framework based on Satisfaction Systems, and Applications to Description Logics

no code implementations8 Feb 2015 Marc Aiguier, Jamal Atif, Isabelle Bloch, Céline Hudelot

Belief revision of knowledge bases represented by a set of sentences in a given logic has been extensively studied but for specific logics, mainly propositional, and also recently Horn and description logics.

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