33 citations · 48 across the 6 of their papers we have counts for
7 papers · 1 filter
UNCOVER: Unknown Class Object Detection for Autonomous Vehicles in Real-time
Lars Schmarje, Kaspar Sakman, Reinhard Koch +1
Autonomous driving (AD) operates in open-world scenarios, where encountering unknown objects is inevitable. However, standard object detectors trained on a limited number of base c…
Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation
Lars Schmarje, Vasco Grossmann, Claudius Zelenka +2
In the field of image classification, existing methods often struggle with biased or ambiguous data, a prevalent issue in real-world scenarios. Current strategies, including semi-s…
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…
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…
Life is not black and white -- Combining Semi-Supervised Learning with fuzzy labels
Lars Schmarje, Reinhard Koch
The required amount of labeled data is one of the biggest issues in deep learning. Semi-Supervised Learning can potentially solve this issue by using additional unlabeled data. How…
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…