Label Propagation for Deep Semi-supervised Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00335654" target="_blank" >RIV/68407700:21230/19:00335654 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/8954421" target="_blank" >https://ieeexplore.ieee.org/document/8954421</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPR.2019.00521" target="_blank" >10.1109/CVPR.2019.00521</a>

Result language

angličtina

Original language name

Label Propagation for Deep Semi-supervised Learning

Original language description

Semi-supervised learning is becoming increasingly important because it can combine data carefully labeled by humans with abundant unlabeled data to train deep neural networks. Classic methods on semi-supervised learning that have focused on transductive learning have not been fully exploited in the inductive framework followed by modern deep learning. The same holds for the manifold assumption---that similar examples should get the same prediction. In this work, we employ a transductive label propagation method that is based on the manifold assumption to make predictions on the entire dataset and use these predictions to generate pseudo-labels for the unlabeled data and train a deep neural network. At the core of the transductive method lies a nearest neighbor graph of the dataset that we create based on the embeddings of the same network. Therefore our learning process iterates between these two steps. We improve performance on several datasets especially in the few labels regime and show that our work is complementary to current state of the art.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

CVPR 2019: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition

ISBN

978-1-7281-3293-8

ISSN

1063-6919

e-ISSN

2575-7075

Number of pages

10

Pages from-to

5065-5074

Publisher name

IEEE

Place of publication

—

Event location

Long Beach

Event date

Jun 15, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—