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Found 6 papers, 5 papers with code
Is one annotation enough? A data-centric image classification benchmark for noisy and ambiguous label estimation
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.
Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy
We propose a novel loss to improve the overclustering capability of our framework and show the benefit of overclustering for fuzzy labels.
S2C2 - An orthogonal method for Semi-Supervised Learning on ambiguous labels
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
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.
Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclustering
We propose a novel loss to improve the overclustering capability of our framework and show on the common image classification dataset STL-10 that it is faster and has better overclustering performance than previous work.
MorphoCluster: Efficient Annotation of Plankton images by Clustering
By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator and allows experts to adapt the granularity of their sorting scheme to the structure in the data.
