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Found 4 papers, 3 papers with code
Annotating Ambiguous Images: General Annotation Strategy for High-Quality Data with Real-World Biomedical Validation
In the field of image classification, existing methods often struggle with biased or ambiguous data, a prevalent issue in real-world scenarios.
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.
Beyond Hard Labels: Investigating data label distributions
High-quality data is a key aspect of modern machine learning.
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.
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