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Status: Unchecked ×Keyword: hard example mining ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Focal Loss for Dense Object Detection

   Lin, Goyal, Girshick, He and Dollár · arXiv (Cornell University) · 2017

   The paper proposes the Focal Loss, which down-weights easy examples, and shows a simple dense detector, RetinaNet, trained with it matching one-stage speed while surpassing two-stage accuracy.

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   1. UncheckedThe paper states that extreme imbalance between background and object examples during training is the main reason one-stage detectors have been less accurate.“We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause.”
   2. UncheckedThe paper's Focal Loss concentrates training on a small set of hard examples so the many easy negatives do not overwhelm the detector.“Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.”