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Weakly Supervised Image Classification Through Noise Regularization
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Weakly supervised learning is an essential problem in computer vision tasks, such as image classification, object recognition, etc., because it is expected to work in the scenarios where a large dataset with clean labels is not available. While there are a number of studies on weakly supervised image classification, they usually limited to either single-label or multi-label scenarios. In this work, we propose an effective approach for weakly supervised image classification utilizing massive
doi:10.1109/cvpr.2019.01178
dblp:conf/cvpr/HuHSC19
fatcat:ejn3k6samrcslhqfx7yn7rm7py