Modern CNN-based object detectors assign anchors for ground-truth objects
under the restriction of object-anchor Intersection-over-Unit (IoU). In this
study, we propose a learning-to-match approach to break IoU restriction,
allowing objects to match anchors in a flexible manner. Our approach, referred
to as FreeAnchor, updates hand-crafted anchor assignment to "free" anchor
matching by formulating detector training as a maximum likelihood estimation
(MLE) procedure. FreeAnchor targets at learning features which best explain a
class of objects in terms of both classification and localization. FreeAnchor
is implemented by optimizing detection customized likelihood and can be fused
with CNN-based detectors in a plug-and-play manner. Experiments on COCO
demonstrate that FreeAnchor consistently outperforms their counterparts with
significant margins.

本文提出了一种名为 FreeAnchor 的学习对应方法来打破目标 - 锚点 IoU 的限制，更新手工制定的锚点分配为自由的锚点匹配，并通过最大似然估计（MLE）过程来实现检测器训练。该方法通过优化检测有效似然来实现，可以与基于 CNN 的检测器插入并组合使用，实验结果表明，FreeAnchor 在 COCO 数据集上表现优异。

FreeAnchor：学习匹配锚点以进行视觉目标检测

FreeAnchor: Learning to Match Anchors for Visual Object Detection

Probabilistic survival predictions from models trained with Maximum
Likelihood Estimation (MLE) can have high, and sometimes unacceptably high
variance. The field of meteorology, where the paradigm of maximizing sharpness
subject to calibration is popular, has addressed this problem by using scoring
rules beyond MLE, such as the Continuous Ranked Probability Score (CRPS). In
this paper we present the \emph{Survival-CRPS}, a generalization of the CRPS to
the survival prediction setting, with right-censored and interval-censored
variants. We evaluate our ideas on the mortality prediction task using two
different Electronic Health Record (EHR) data sets (STARR and MIMIC-III)
covering millions of patients, with suitable deep neural network architectures:
a Recurrent Neural Network (RNN) for STARR and a Fully Connected Network (FCN)
for MIMIC-III. We compare results between the two scoring rules while keeping
the network architecture and data fixed, and show that models trained with
Survival-CRPS result in sharper predictive distributions compared to those
trained by MLE, while still maintaining calibration.

本文提出了 Survival-CRPS，它是一种广义的连续秩概率得分，用于生存预测设置的正确或区间截断变体，并评估了两个电子健康记录数据集上的死亡率预测任务的效果，结果表明训练 CRPS 得分的模型相较于 MLE 训练的模型拥有更加尖锐的预测分布并保持校准。