一开始我在网上找到了一个pytorch的Ordinal Regression实现spacecutter,但经过一番实验之后我发现它写的并不完美,于是自己又修改了一下,在这里分享给大家 classOrdinalRegressionLoss(nn.Module):def__init__(self,num_class,train_cutpoints=False,scale=20.0):super().__init__()self.num_classes=num_classnum_c...
最后,关于对比方法,其实也只有一个,就是BIFs+OHRank,可能是因为这篇论文比较古老了,是2016的CVPR,所以他对比的方法更加古老,是2011年的,而这会用的还不是CNN,为了避免因为CNN的使用带来的涨点,所以实验部分还提出了一个MR-CNN的方法,如下图: 是在用相同的主干网络,直接加上Euclidean loss,以证明Ordinal Regres...
Monocular depth estimation (MDE), 从理论上是一个病态的问题,近年来的工作利用 deep convolutional neural networks (DCNN) 提取 image-level information 及 hierarchical features 在 MDE 问题上取得了巨大的提升。这些方法把 MDE 视为一种 regression problem,并把 mean squared error (MSE) 作为 regression loss,...
We propose a distance ordinal regression loss for an improved nuclei instance segmentation in digitized tissue specimen images. A convolutional neural network with two decoder branches is built. The first decoder branch conducts the nuclear pixel prediction and the second branch predicts the distance to...
Ordinal Regression Model 下面就是这篇论文《Ordinal Regression with Multiple Output CNN for Age Estimation》的重点了...,这篇论文不同于传统的分类问题或回归问题处理年龄估计,而是引入了一种Ordinal Regression思想,是...
Caffe Loss Layer for Ordinal Regression with Multiple Output CNN for Age Estimation. - luoyetx/OrdinalRegression
作者认为,在单目深度估计时,随着真实深度的增加,预测的误差变大是应当被容忍的,因此提出一种新的计算损失的方法,将深度估计从回归问题转为分类问题,不再预测具体的深度值,而是对深度值所在区间进行分类。 Architecture 网络架构分为三个部分:Dense feature extractor+Scene understanding modular+Ordinal regression Dense ...
本文将年龄预测问题转换为序数回归(ordinal regression)问题,结合CNN实现端到端年龄预测,取得了当时SOTA的性能。 Dataset/Algorithm/Model/Experiment Detail Ordinal Regression Ordinal Regression问题就是样本集的label取自一个rank集合,不同的rank之间有顺序,而问题的求解就是找到一个样本到rank的映射,使得预定义的cost函...
The continuous formulation of the ordinal regression model has the advantage of no loss of precision due to categorization of the scores and no arbitrary choice of the number and boundaries of categories. The semi-parametric form of the model makes it a flexible method for analysis of continuous...
The core elements of the proposed TOR includean objective function that caters to several commonly used loss functionscasted in transductive settings, for general ordinal regression. A labelswapping scheme that facilitates a strictly monotonic decrease in the objectivefunction value is also introduced. ...