Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation

1 code implementation • 13 Jul 2022 • Lars Schmarje, Vasco Grossmann, Claudius Zelenka, Sabine Dippel, Rainer Kiko, Mariusz Oszust, Matti Pastell, Jenny Stracke, Anna Valros, Nina Volkmann, Reinhard Koch

We propose a data-centric image classification benchmark with ten real-world datasets and multiple annotations per image to allow researchers to investigate and quantify the impact of such data quality issues.

Image Classification Noise Estimation

S2C2 - An orthogonal method for Semi-Supervised Learning on ambiguous labels

no code implementations • 29 Sep 2021 • Lars Schmarje, Monty Santarossa, Simon-Martin Schröder, Claudius Zelenka, Rainer Kiko, Jenny Stracke, Nina Volkmann, Reinhard Koch

Semi-Supervised Learning (SSL) can decrease the required amount of labeled image data and thus the cost for deep learning.

A data-centric approach for improving ambiguous labels with combined semi-supervised classification and clustering

1 code implementation • 30 Jun 2021 • Lars Schmarje, Monty Santarossa, Simon-Martin Schröder, Claudius Zelenka, Rainer Kiko, Jenny Stracke, Nina Volkmann, Reinhard Koch

In our data-centric approach, we propose a method to relabel such ambiguous labels instead of implementing the handling of this issue in a neural network.

Classification Clustering