1.通过pixel weighting functions自适应地对每个像素的contribution(实际展现的loss)进行re-weighting 引起更高loss的像素的权重更大,这直接对潜在的类内和类间不平衡进行了补偿 Focus on a family of weighting functions with boundedp-normand∞-norm 2.通过普通的max-pooling在pixel-loss level上对pixel weighting ...
在图像语义分割任务中,"Loss Max-Pooling for Semantic Image Segmentation"是一种优化损失函数的方法,旨在改进传统损失函数在处理此类问题时的性能。图像语义分割是计算机视觉领域的一个关键任务,它的目标是将图像的每个像素分类到预定义的类别中,例如区分人、车、建筑等对象。 传统的语义分割方法通常采用交叉熵损失(...
segmentationdatasets增加了更多的minority classes,这使得权重的划分更复杂 所以这篇文章提出了一种新的解决方法:LossMax-Pooling主要思想 1.通过...CVPR2017收录,思想很明确,但是进行了很多数学证明,奈何数学功底不够啊,所以欢迎多多讨论交流。本文主要解决的是semanticsegmentation中imbalanced training ...
(KWS-LSTM)Max-pooling loss training of long short-term memory networks for small-footprint KWS,程序员大本营,技术文章内容聚合第一站。
We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained net...