Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00376838" target="_blank" >RIV/68407700:21230/24:00376838 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/WACV57701.2024.00033" target="_blank" >https://doi.org/10.1109/WACV57701.2024.00033</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/WACV57701.2024.00033" target="_blank" >10.1109/WACV57701.2024.00033</a>

Result language

angličtina

Original language name

Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning

Original language description

Deep active learning in the presence of outlier examples poses a realistic yet challenging scenario. Acquiring unlabeled data for annotation requires a delicate balance between avoiding outliers to conserve the annotation budget and prioritizing useful inlier examples for effective training. In this work, we present an approach that leverages three highly synergistic components, which are identified as key ingredients: joint classifier training with inliers and outliers, semi-supervised learning through pseudo-labeling, and model ensembling. Our work demonstrates that ensembling significantly enhances the accuracy of pseudolabeling and improves the quality of data acquisition. By enabling semi-supervision through the joint training process, where outliers are properly handled, we observe a substantial boost in classifier accuracy through the use of all available unlabeled examples. Notably, we reveal that the integration of joint training renders explicit outlier detection unnecessary; a conventional component for acquisition in prior work. The three key components align seamlessly with numerous existing approaches. Through empirical evaluations, we showcase that their combined use leads to a performance increase. Remarkably, despite its simplicity, our proposed approach outperforms all other methods in terms of performance. Code: https://github.com/vladan-stojnic/active-outliers

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

2024

Confidentiality

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

Article name in the collection

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

ISBN

979-8-3503-1892-0

ISSN

2472-6737

e-ISSN

2642-9381

Number of pages

10

Pages from-to

259-268

Publisher name

IEEE

Place of publication

Piscataway

Event location

Waikoloa, HI, USA

Event date

Jan 4, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001222964600026