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Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems.
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Mask r-cnn
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Focal loss for dense object detection
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Regularizing neural networks by penalizing confident output distributions
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Imbalance problems in object detection: A review
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Pv-rcnn: Point-voxel feature set abstraction for 3d object detection
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Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, and Eric Xing · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert R Sabuncu · 2018
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Class-balanced loss based on effective number of samples
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Bag of tricks for image classification with convolutional neural networks
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Can gradient clipping mitigate label noise?
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Well-classified examples are underestimated in classification with deep neural networks
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