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20202026
most citedFuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy

14 citations · 14 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.CV2026

Don't Mind the Gaps: Implicit Neural Representations for Resolution-Agnostic Retinal OCT Analysis

Bennet Kahrs, Julia Andresen, Fenja Falta +3

Routine clinical imaging of the retina using optical coherence tomography (OCT) is performed with large slice spacing, resulting in highly anisotropic images and a sparsely scanned…

cs.CV2023

Label Smarter, Not Harder: CleverLabel for Faster Annotation of Ambiguous Image Classification with Higher Quality

Lars Schmarje, Vasco Grossmann, Tim Michels +4

High-quality data is crucial for the success of machine learning, but labeling large datasets is often a time-consuming and costly process. While semi-supervised learning can help…

cs.CV202114 cited

Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy

Lars Schmarje, Johannes Brünger, Monty Santarossa +3

Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large…

cs.CV2021

Learning Stixel-based Instance Segmentation

Monty Santarossa, Lukas Schneider, Claudius Zelenka +3

Stixels have been successfully applied to a wide range of vision tasks in autonomous driving, recently including instance segmentation. However, due to their sparse occurrence in t…

cs.CV2020

Beyond Cats and Dogs: Semi-supervised Classification of fuzzy labels with overclustering

Lars Schmarje, Johannes Brünger, Monty Santarossa +3

A long-standing issue with deep learning is the need for large and consistently labeled datasets. Although the current research in semi-supervised learning can decrease the require…