Label Propagation for Deep Semi-supervised Learning - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2019

Label Propagation for Deep Semi-supervised Learning

Abstract

Semi-supervised learning is becoming increasingly important because it can combine data carefully labeled by humans with abundant unlabeled data to train deep neu-ral 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.
Fichier principal
Vignette du fichier
1904.04717.pdf (611.55 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02370207 , version 1 (19-11-2019)

Identifiers

Cite

Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum. Label Propagation for Deep Semi-supervised Learning. 2019. ⟨hal-02370207⟩
174 View
161 Download

Altmetric

Share

Gmail Facebook X LinkedIn More