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Found 7 papers, 4 papers with code
Label Smarter, Not Harder: CleverLabel for Faster Annotation of Ambiguous Image Classification with Higher Quality
High-quality data is crucial for the success of machine learning, but labeling large datasets is often a time-consuming and costly process.
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.
Learning Stixel-based Instance Segmentation
Stixels have been successfully applied to a wide range of vision tasks in autonomous driving, recently including instance segmentation.
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.
A survey on Semi-, Self- and Unsupervised Learning for Image Classification
In this survey, we provide an overview of often used ideas and methods in image classification with fewer labels.
