Twin neural network regression is trained to predict differences between
regression targets rather than the targets themselves. A solution to the
original regression problem can be obtained by ensembling predicted differences
between the targets of an unknown data point and multiple known anchor data
points. Choosing the anchors to be the nearest neighbors of the unknown data
point leads to a neural network-based improvement of k-nearest neighbor
regression. This algorithm is shown to outperform both neural networks and
k-nearest neighbor regression on small to medium-sized data sets.

双神经网络回归通过训练预测回归目标之间的差异而不是目标本身。该算法通过集成未知数据点的目标与多个已知锚点的差异来解决原始回归问题，其中锚点选择为未知数据点的最近邻，证明在小到中等规模的数据集上表现优于神经网络和最近邻回归。

双神经网络优化的 k 最近邻回归

Twin Neural Network Improved k-Nearest Neighbor Regression

We present ObjectBox, a novel single-stage anchor-free and highly
generalizable object detection approach. As opposed to both existing
anchor-based and anchor-free detectors, which are more biased toward specific
object scales in their label assignments, we use only object center locations
as positive samples and treat all objects equally in different feature levels
regardless of the objects' sizes or shapes. Specifically, our label assignment
strategy considers the object center locations as shape- and size-agnostic
anchors in an anchor-free fashion, and allows learning to occur at all scales
for every object. To support this, we define new regression targets as the
distances from two corners of the center cell location to the four sides of the
bounding box. Moreover, to handle scale-variant objects, we propose a tailored
IoU loss to deal with boxes with different sizes. As a result, our proposed
object detector does not need any dataset-dependent hyperparameters to be tuned
across datasets. We evaluate our method on MS-COCO 2017 and PASCAL VOC 2012
datasets, and compare our results to state-of-the-art methods. We observe that
ObjectBox performs favorably in comparison to prior works. Furthermore, we
perform rigorous ablation experiments to evaluate different components of our
method. Our code is available at: this https URL

ObjectBox 是一种新颖的单阶段无锚点、高度通用的物体检测方法，通过仅使用对象中心位置作为正样本以及不受对象大小或形状影响地在不同的特征级别平等对待所有对象，解决了现有基于锚点和无锚点检测器在标签分配中偏向特定对象尺度的问题。