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Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
动机: In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task’s loss. We propose a principled approach to multi-task deep learning which weighs multiple loss functions b[论文阅读] Annotation-Efficient Cell Counting
论文地址:https://doi.org/10.1007/978-3-030-87237-3_39 代码:https://github.com/cvbmi-research/AnnotationEfficient-CellCounting 发表于:MICCAI 21 Abstract 最近深度学习的进展在显微镜细胞计数任务上取得了令人印象深刻的结果。深度学习模型的成功通常需要足够的训练数sss
Density-based sampling These types of strategies take into account the distribution and local density. The intuition is that the location with more density is more likely to be queried. i.e. the selected instances and the unlabeled instances should have sUncertainty——CVPR 2021
1. Uncertainty Guided Collaborative Training for Weakly Supervised Temporal Action Detection 用视频级别的类别标签进行弱监督的实时动作检测,作者提出了一种不确定性指导的协同训练 UGCT,包含一个线上伪标签生成模型用于 RGB 和光流的互相学习,此外是一个不确定性感知DUL模型详解——代码+原理——Data Uncertainty Learning
一、背景 二、原理 三、模型结构 四、核心代码 五、效果和优缺点 参考: 【1】旷视研究院提出数据不确定性算法 DUL,优化人脸识别性能 - 知乎 【2】【ICCV2019】probabilistic face embeddings 概率人脸嵌入_木盏-CSDN博客 【3】http://openaccess.thecvf.com/content_ICCV_2论文笔记—Robust Localization Using 3D NDT Scan Matching with Experimentally Determined Uncertainty and R
论文笔记—Robust Localization Using 3D NDT Scan Matching with Experimentally Determined Uncertainty and Road Marker Matching 文章摘要 ~~~~匹配不确定性和不匹配不确定性(matched and unmatched uncertainty)
匹配不确定性和不匹配不确定性(matched and unmatched uncertainty) 参考:https://www.zhihu.com/question/47182140?from=profile_question_card 1. 匹配扰动: 和控制出现在同一个通道(或者经过变换之后)的扰动和不确定性;其他为非匹配扰动; 2. 如果我们知道扰动(或者通过扰动观测Understanding uncertainty modeling (including Bayesian DL and Deep GP): Applications to X
理解不确定性的实际应用: https://zhuanlan.zhihu.com/p/151398233 一文了解目标检测边界框概率分布 https://blog.csdn.net/wyhz56/article/details/103375012 Fusion of LiDAR and Camera Sensor Data for Environment Sensing in Driverless Vehicles (with Gaussian Process) hAUGMIX : A SIMPLE DATA PROCESSING METHOD TO IMPROVE ROBUSTNESS AND UNCERTAINTY
目录概主要内容实验的指标 Dan Hendrycks, Norman Mu,, et. al, AUGMIX : A SIMPLE DATA PROCESSING METHOD TO IMPROVE ROBUSTNESS AND UNCERTAINTY. 概 本文介绍AUGMIX算法——对现有的的一些augmentation方法进行混用, 并构建了一个新的损失函数. 主要内容 其中\(\mathrm{英语四级-词汇(三)
-ply-:折叠 imply 表明,说明 infer 推测,推理 complicate 使复杂化 duplicate 复制,副本,复制的 bicycle 自行车 explicit贝叶斯深度学习
贝叶斯公式 贝叶斯深度学习 贝叶斯神经网络该怎么用? 网络的权重和偏置都是分布,想要像非贝叶斯神经网络那样进行前向传播(feed-forward),可以对贝叶斯神经网络的权重和偏置进行采样,得到一组参数,然后像非贝叶斯神经网络那样即可。 当然,我们可以对权重和偏置的分布进行多次采样Visual-Based Autonomous Driving Deployment from a Stochastic and Uncertainty-Aware Perspective
张宁 Visual-Based Autonomous Driving Deployment from a Stochastic and Uncertainty-Aware Perspective Lei Tai Peng Yun Yuying Chen Congcong Liu Haoyang Ye Ming Liu 从随机和不确定性角度出发的基于视觉的自动驾驶部署链接:https://pan.baidu.com/s/1iako8pSu9nuwCzIfF_M2Python不确定性软件包中的零除错误
为什么发生以下零除错误? >>> from uncertainties import ufloat >>> a = ufloat((0,0)) >>> x = ufloat((0.3,0.017)) >>> a**x Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/python-Pystan后不确定性间隔
我看到on another forum PyStan与RStan的功能不同,后者使用posterior_interval(),但是我们可以使用numpy.percentile().我目前正在使用PyStan中的pystan.StanModel.optimizing()函数来获取使后验可能性最大化的参数集.我现在也想获得后验结果的外部95%置信区间,因此我想知道numpy.pe如何在lmfit最小二乘最小化中包含我的数据错误,以及lmfit中conf_interval2d函数的这个错误是什么?
我是python的新手,并试图使用lmfit包检查我自己的计算,但是我不确定(1)如何包含错误的以下测试(和2)的数据(sig)的错误得到conf_interval2d如下所示): import numpy as np from lmfit import Parameters, Minimizer, conf_interval, conf_interval2d, minimize, printfuncsBENG0091 Stochastic Calculus & Uncertainty Analysis
Department of Biochemical EngineeringBENG0091 Stochastic Calculus & Uncertainty AnalysisCoursework 2To be submitted on Moodle by 22-March-2019 (23:55)The company you work for (Pipes and Tubing for All, PTFA) has tasked you with assessing thecharacteriCTS的前世今生
Clock Tree Synthesis,时钟树综合,简称CTS。时钟树综合就是建立一个时钟网络,使时钟信号能够传递到各个时序器件。CTS是布局之后相当重要的一个步骤,在现如今集成了上亿个晶体管的芯片上,如何设计一个合理的时钟网络,是一件非常具有挑战性的事情。个人认为相比于place和route更依赖工具的