A machine learning (ML) feature network is a graph that connects ML features
in learning tasks based on their similarity. This network representation allows
us to view feature vectors as functions on the network. By leveraging function
operations from Fourier analysis and from functional analysis, one can easily
generate new and novel features, making use of the graph structure imposed on
the feature vectors. Such network structures have previously been studied
implicitly in image processing and computational biology. We thus describe
feature networks as graph structures imposed on feature vectors, and provide
applications in machine learning. One application involves graph-based
generalizations of convolutional neural networks, involving structured deep
learning with hierarchical representations of features that have varying depth
or complexity. This extends also to learning algorithms that are able to
generate useful new multilevel features. Additionally, we discuss the use of
feature networks to engineer new features, which can enhance the expressiveness
of the model. We give a specific example of a deep tree-structured feature
network, where hierarchical connections are formed through feature clustering
and feed-forward learning. This results in low learning complexity and
computational efficiency. Unlike "standard" neural features which are limited
to modulated (thresholded) linear combinations of adjacent ones, feature
networks offer more general feedforward dependencies among features. For
example, radial basis functions or graph structure-based dependencies between
features can be utilized.

机器学习中的特征网络是连接具有相似性的学习任务中的特征的图形表示，通过充分利用傅里叶分析和函数分析中的函数操作，可以轻松生成新的特征，应用于机器学习中的图形化卷积神经网络扩展到具有不同深度或复杂度特征的分层表示，并讨论了使用特征网络工程化新特征以提高模型的表达能力。

机器学习与应用中的特征网络方法

Feature Network Methods in Machine Learning and Applications

This work proposed a novel deep registration pipeline for 3D CT and 2D U/S
kidney scans of free breathing, which consists of a feature network, and a
3D-2D CNN-based registration network. The feature network has handcraft texture
feature layers to reduce the semantic gap. The registration network is
encoder-decoder structure with loss of feature-image-motion (FIM), which
enables hierarchical regression at decoder layers and avoids multiple network
concatenation. It was first pretrained with retrospective datasets cum training
data generation strategy, then adapted to specific patient data under
unsupervised one-cycle transfer learning in onsite application. The experiment
was on 132 U/S sequences, 39 multiple phase CT and 210 public single phase CT
images, and 25 pairs of CT and U/S sequences. It resulted in mean contour
distance (MCD) of 0.94 mm between kidneys on CT and U/S images and MCD of 1.15
mm on CT and reference CT images. For datasets with small transformations, it
resulted in MCD of 0.82 and 1.02 mm respectively. For large transformations, it
resulted in MCD of 1.10 and 1.28 mm respectively. This work addressed
difficulties in 3DCT-2DUS kidney registration during free breathing via novel
network structures and training strategy.

本文提出了一种新的深度配准管道，用于自由呼吸的 3D CT 和 2D U/S 肾脏扫描，包括一个特征网络和一个基于 3D-2D CNN 的配准网络，该网络结构和训练策略通过降低语义差距等手段解决了 3D CT-2D U/S 肾脏配准时的困难。

KidneyRegNet: 一种用于呼吸期 3DCT-2DUS 肾脏配准的深度学习方法

KidneyRegNet: A Deep Learning Method for 3DCT-2DUS Kidney Registration  during Breathing

Robustness and discrimination power are two fundamental requirements in
visual object tracking. In most tracking paradigms, we find that the features
extracted by the popular Siamese-like networks cannot fully discriminatively
model the tracked targets and distractor objects, hindering them from
simultaneously meeting these two requirements. While most methods focus on
designing robust correlation operations, we propose a novel target-dependent
feature network inspired by the self-/cross-attention scheme. In contrast to
the Siamese-like feature extraction, our network deeply embeds cross-image
feature correlation in multiple layers of the feature network. By extensively
matching the features of the two images through multiple layers, it is able to
suppress non-target features, resulting in instance-varying feature extraction.
The output features of the search image can be directly used for predicting
target locations without extra correlation step. Moreover, our model can be
flexibly pre-trained on abundant unpaired images, leading to notably faster
convergence than the existing methods. Extensive experiments show our method
achieves the state-of-the-art results while running at real-time. Our feature
networks also can be applied to existing tracking pipelines seamlessly to raise
the tracking performance. Code will be available.

我们提出了一种基于自注意力 / 交叉注意力的目标依赖特征网络，通过深度嵌入多层特征的交叉图像特征相关性，抑制了非目标特征，从而实现了实例变化的特征提取，在不需要额外的相关步骤的情况下可以直接用于预测目标位置，且可以灵活地在丰富的无配对图像上进行预训练，实现了较快的收敛速度和最先进的实时跟踪性能。