[论文精读] GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks 关于我独自升级 三分侠气,一点素心8 人赞同了该文章 想结交一些AI/机器人/控制领域的博士生 | 本人是江南大学控制科学的在读博士生,由于组内没有AI相关研究领域的博士生、硕士生,因此借助这个平台征集可以一起...
However, the design of the SM capacitance value is not given, and extra loss is brought in. This study gives the design of the SM capacitance value when the second-harmonic circulating current is injected with a closed-loop control strategy. Then, an adaptive control method based on SM ...
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks 论文阅读笔记,程序员大本营,技术文章内容聚合第一站。
paper170:CVPR2020网络量化和压缩 Adaptive loss-aware quantization for multi-bi networks 要点简介 1、概括性介绍 1)无论是图像压缩/视频压缩,还是其他典型的任务,因为参数的全量化精度开销很大,所以部署问题就变得很关键。 2)神经网络量化本身就是为了追求压缩比和性能之间的平衡,量化分为均匀量化、非均匀量化和细...
最近,我看到一篇由Jon Barron在CVPR 2019中提出的关于为机器学习问题开发一个鲁棒、自适应的损失函数的文章。本文是对 A General and Adaptive Robust Loss Function 一些必要概念的回顾,它还将包含一个简单回归问题上的损失函数的实现。 关于异常值和鲁棒的损失的问题 ...
模型数量 18 智能监控 模型数量 15 水平线估计 模型数量 6 使用「Adaptive Robust Loss(Adaptive Loss)」的项目 3 模型资源 1 项目文献 车辆重新识别 2021年 SOTA! ON VeRi-776 mAP 87.1 智能监控 2021年 SOTA! ON VeRi-776 mAP 87.1 ConvLSTM (Huber Loss, naive residual path) ...
Discoveries of adaptive gene knockouts and widespread losses of complete genes have in recent years led to a major rethink of the early view that loss-of-function alleles are almost always deleterious. Today, surveys of population genomic diversity are r
(2) We design an adaptive loss weighting algorithm to weight the loss function of the four tasks, hoping to achieve the balance training of the four tasks by limiting the loss function to the same order of magnitude [23], [24], [25]. (3) Apply APINNs to solve solitary wave solutions...
目前主流的热图损失函数都是均方误差(MSE),在本文中则根据Wing Loss的启发,提出了一种新的针对热图的损失函数---Adaptive Wing Loss。为了解决前景像素和背景像素之间的不平衡问题,还提出了Weighted Loss Map。为了进一步提高人脸对齐精度,还引入了boundary prediction 和 CoordConv。 论文贡献 受Wing Loss[1]启发,提...
Secondly, loss functions suitable for class imbalances are proposed, including adaptive margin perception loss and adaptive hard triplet loss, the former offsets inter-class confusion of classifiers, alleviating the imbalance issue inherent in pseudo-label generation. The latter effectively tackles the ...