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...
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...
Caffe Loss Layer for Ordinal Regression with Multiple Output CNN for Age Estimation. - luoyetx/OrdinalRegression
首先要说明的是逻辑回归解决的是分类问题，不是回归问题，而ordinal regression则更模糊些，可以理解为解决...
Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression Qiang Li∗, Jingjing Wang∗, Zhaoliang Yao, Yachun Li, Pengju Yang, Jingwei Yan, Chunmao Wang, Shiliang Pu† Hikvision Research Institute, China {liqiang...
simplifies the logical complexity and implementation complexity of the model, so that the loss function can be easily customized for any special metric. We have designed loss functions for various ordinal regression evaluation metrics and achieved the best level of results on many ordinal regression ...
We present nondeterministic hypotheses learned from an ordinal regression task. They try to predict the true rank for an entry, but when the classification is uncertain the hypotheses predict a set of consecutive ranks (an interval). The aim is to keep t
regression model where biting rates were classified as “low” (0–0.09/hr), “medium” (0.1–0.9/hr) and “high” (1.0–3.8/hr). We have improved on this modeling approach by avoiding the arbitrariness associated with data discretization, and the resulting loss of information, and by ...
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 ...