Publications associées à Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

A Learning-Based Framework for Quantized Compressed Sensing

Volkan Cevher, Rabeeh Karimi Mahabadi, Junhong Lin

Sparse recovery from undersampled random quan- tization measurements is a recent active research topic. Previous work asserts that stable recovery can be guaranteed via the basis pursuit dequantizer (BPDQ) if the measurements number is large enough, consid ...

2019

Dual Generator Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Yan Yan, Wei Wang

State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data. However, these methods require the training of one specific model for every ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Learning to Find Unpaired Cross-Spectral Correspondences

Seungryong Kim

We present a deep architecture and learning framework for establishing correspondences across cross-spectral visible and infrared images in an unpaired setting. To overcome the unpaired cross-spectral data problem, we design the unified image translation a ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

SGAN: An Alternative Training of Generative Adversarial Networks

François Fleuret, Tatjana Chavdarova

The Generative Adversarial Networks (GANs) have demonstrated impressive performance for data synthesis, and are now used in a wide range of computer vision tasks. In spite of this success, they gained a reputation for being difficult to train, what results ...

IEEE2018

Lower Bounds on Active Learning for Graphical Model Selection

Volkan Cevher, Jonathan Mark Scarlett

We consider the problem of estimating the underlying graph associated with a Markov random field, with the added twist that the decoding algorithm can iteratively choose which subsets of nodes to sample based on the previous samples, resulting in an active ...

2017

Object Detection with Active Sample Harvesting

Olivier Canévet

The work presented in this dissertation lies in the domains of image classification, object detection, and machine learning. Whether it is training image classifiers or object detectors, the learning phase consists in finding an optimal boundary between po ...

École Polytechnique Fédérale de Lausanne2017

Active Learning and Proofreading for Delineation of Curvilinear Structures

Pascal Fua, Agata Justyna Mosinska, Jakub Tarnawski

Many state-of-the-art delineation methods rely on supervised machine learning algorithms. As a result, they require manually annotated training data, which is tedious to obtain. Furthermore, even minor classification errors may significantly affect the top ...

2017

Learning Active Learning from Data

Pascal Fua, Raphael Sznitman, Ksenia Konyushkova

In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection proced ...

2017

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

Graph Chatbot

Chattez avec Graph Search

A Learning-Based Framework for Quantized Compressed Sensing

Dual Generator Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Learning to Find Unpaired Cross-Spectral Correspondences

Federated Learning with Bayesian Differential Privacy

SGAN: An Alternative Training of Generative Adversarial Networks

Lower Bounds on Active Learning for Graphical Model Selection

Object Detection with Active Sample Harvesting

Object Detection with Active Sample Harvesting

Active Learning and Proofreading for Delineation of Curvilinear Structures

Learning Active Learning from Data

Federated Learning with Bayesian Differential Privacy

Lower Bounds on Active Learning for Graphical Model Selection

SGAN: An Alternative Training of Generative Adversarial Networks

A Learning-Based Framework for Quantized Compressed Sensing

Dual Generator Generative Adversarial Networks for Multi-domain Image-to-Image Translation

Learning to Find Unpaired Cross-Spectral Correspondences

Object Detection with Active Sample Harvesting

Object Detection with Active Sample Harvesting

Learning Active Learning from Data

Active Learning and Proofreading for Delineation of Curvilinear Structures