7 citations · 13 across the 6 of their papers we have counts for
3 papers · 1 filter
Towards Reducing Data Acquisition and Labeling for Defect Detection using Simulated Data
Lukas Malte Kemeter, Rasmus Hvingelby, Paulina Sierak +2
In many manufacturing settings, annotating data for machine learning and computer vision is costly, but synthetic data can be generated at significantly lower cost. Substituting th…
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning
Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok +1
Do we need active learning? The rise of strong deep semi-supervised methods raises doubt about the usability of active learning in limited labeled data settings. This is caused by…
Deep Semi-Supervised Learning for Time Series Classification
Jann Goschenhofer, Rasmus Hvingelby, David Rügamer +3
While Semi-supervised learning has gained much attention in computer vision on image data, yet limited research exists on its applicability in the time series domain. In this work,…