论文标题
语义分割的损失功能的调查
A survey of loss functions for semantic segmentation
论文作者
论文摘要
图像分割一直是一个积极的研究领域，因为它具有广泛的应用，从自动疾病检测到自动驾驶汽车。在过去的五年中，各种论文都提出了不同的客观损失函数，例如在不同的情况下，例如偏见的数据，稀疏分割等。在本文中，我们总结了一些广泛用于图像分割的众所周知的损失函数，并列出了它们的用法可以帮助快速和更好地收敛模型的情况。此外，我们还引入了新的日志骰子损失函数，并比较了其在NBFS头骨分割开源数据集中具有广泛使用损失功能的性能。我们还展示了某些损失功能在所有数据集中的表现都很好，并且可以作为未知数据分配方案的良好基线选择。我们的代码可在Github：https：//github.com/shruti-jadon/semantic-semantic-sementation-loss-functions上找到。
Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease detection to self-driving cars. In the past five years, various papers came up with different objective loss functions used in different cases such as biased data, sparse segmentation, etc. In this paper, we have summarized some of the well-known loss functions widely used for Image Segmentation and listed out the cases where their usage can help in fast and better convergence of a model. Furthermore, we have also introduced a new log-cosh dice loss function and compared its performance on the NBFS skull-segmentation open-source data-set with widely used loss functions. We also showcased that certain loss functions perform well across all data-sets and can be taken as a good baseline choice in unknown data distribution scenarios. Our code is available at Github: https://github.com/shruti-jadon/Semantic-Segmentation-Loss-Functions.
