Methods › General › Loss Functions › Self-Adjusting Smooth L1 Loss
Self-Adjusting Smooth L1 Loss
Introduced by Cheng-Yang Fu et al. in RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Self-Adjusting Smooth L1 Loss is a loss function used in object detection that was introduced with RetinaMask. This is an improved version of Smooth L1. For Smooth L1 loss we have:
f(x) = 0.5 x²/β if |x| < β f(x) = |x| -0.5β otherwise
Here a point β splits the positive axis range into two parts: L2 loss is used for targets in range [0, β], and L1 loss is used beyond β to avoid over-penalizing utliers. The overall function is smooth (continuous, together with its derivative). However, the choice of control point (β) is heuristic and is usually done by hyper parameter search.
Instead, with self-adjusting smooth L1 loss, inside the loss function the running mean and variance of the absolute loss are recorded. We use the running minibatch mean and variance with a momentum of $0.9$ to update these two parameters.
Papers archive 2025-07-28
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RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free 10 Jan 2019 · 53 repositories · arXiv:1901.03353
Tasks archive 2025-07-28
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| Task | Papers |
|---|---|
| Object Detection | 1 |
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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