Weakly Supervised Learning: Cheaper Labels, Smarter Models
Weakly Supervised Learning trains models on cheap, imprecise labels to perform complex tasks. It's used for object detection when you only have image-level tags, not pixel-perfect annotations.
Weakly Supervised Learning trains a model for a specific task, like drawing a box around a car, using only general labels, like 'this image contains a car'. It's a bargain: you trade label precision for lower annotation costs and larger datasets. This is common in computer vision for object detection where full labeling is too expensive. The main footgun is that the model can learn spurious correlations—if all 'boat' images are on water, it might think water is part of the boat.
Read the original → hbilen.github.io
- #machine learning
- #computer vision
- #data labeling
- #deep learning
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