Learning to Complement with Multiple Humans

arXiv:2311.13172v2 Announce Type: replace
Abstract: Real-world image classification tasks tend to be complex, where expert labellers are sometimes unsure about the classes present in the images, leading to the issue of learning with noisy labels (LNL). The ill-posedness of the LNL task requires the adoption of strong assumptions or the use of multiple noisy labels per training image, resulting in accurate models that work well in isolation but fail to optimise human-AI collaborative classification (HAI-CC). Unlike such LNL methods, HAI-CC …

abstract adoption arxiv assumptions classification cs.cv expert humans image images issue labels multiple tasks type world