Recently, feature relation learning has drawn widespread attention in
cross-spectral image patch matching. However, existing related research focuses
on extracting diverse relations between image patch features and ignores
sufficient intrinsic feature representations of individual image patches.
Therefore, an innovative relational representation learning idea is proposed
for the first time, which simultaneously focuses on sufficiently mining the
intrinsic features of individual image patches and the relations between image
patch features. Based on this, we construct a lightweight Relational
Representation Learning Network (RRL-Net). Specifically, we innovatively
construct an autoencoder to fully characterize the individual intrinsic
features, and introduce a Feature Interaction Learning (FIL) module to extract
deep-level feature relations. To further fully mine individual intrinsic
features, a lightweight Multi-dimensional Global-to-Local Attention (MGLA)
module is constructed to enhance the global feature extraction of individual
image patches and capture local dependencies within global features. By
combining the MGLA module, we further explore the feature extraction network
and construct an Attention-based Lightweight Feature Extraction (ALFE) network.
In addition, we propose a Multi-Loss Post-Pruning (MLPP) optimization strategy,
which greatly promotes network optimization while avoiding increases in
parameters and inference time. Extensive experiments demonstrate that our
RRL-Net achieves state-of-the-art (SOTA) performance on multiple public
datasets. Our code will be made public later.

通过创新的关系表示学习方法，同时关注个体内在特征和图像补丁特征之间的关系，构建了一种轻量级的关系表示学习网络（RRL-Net），包括自动编码器、特征交互学习模块、多维全局到局部注意力机制以及基于注意力的轻量级特征提取网络。经过大量实验验证，该网络在多个公共数据集上取得了最先进的性能。

跨光谱图像块匹配的关系表示学习网络

Relational Representation Learning Network for Cross-Spectral Image  Patch Matching

Click-through rate (CTR) estimation plays as a core function module in
various personalized online services, including online advertising, recommender
systems, and web search etc. From 2015, the success of deep learning started to
benefit CTR estimation performance and now deep CTR models have been widely
applied in many industrial platforms. In this survey, we provide a
comprehensive review of deep learning models for CTR estimation tasks. First,
we take a review of the transfer from shallow to deep CTR models and explain
why going deep is a necessary trend of development. Second, we concentrate on
explicit feature interaction learning modules of deep CTR models. Then, as an
important perspective on large platforms with abundant user histories, deep
behavior models are discussed. Moreover, the recently emerged automated methods
for deep CTR architecture design are presented. Finally, we summarize the
survey and discuss the future prospects of this field.

本文综述了 CTR 估算任务中使用的深度学习模型，包括从浅层到深度 CTR 模型的转换，深度 CTR 模型的显式特征交互学习模块，用户行为模型，以及深度 CTR 架构设计的自动化方法。

深度学习用于点击率估计

Deep Learning for Click-Through Rate Estimation

Learning effective feature crosses is the key behind building recommender
systems. However, the sparse and large feature space requires exhaustive search
to identify effective crosses. Deep & Cross Network (DCN) was proposed to
automatically and efficiently learn bounded-degree predictive feature
interactions. Unfortunately, in models that serve web-scale traffic with
billions of training examples, DCN showed limited expressiveness in its cross
network at learning more predictive feature interactions. Despite significant
research progress made, many deep learning models in production still rely on
traditional feed-forward neural networks to learn feature crosses
inefficiently.
In light of the pros/cons of DCN and existing feature interaction learning
approaches, we propose an improved framework DCN-V2 to make DCN more practical
in large-scale industrial settings. In a comprehensive experimental study with
extensive hyper-parameter search and model tuning, we observed that DCN-V2
approaches outperform all the state-of-the-art algorithms on popular benchmark
datasets. The improved DCN-V2 is more expressive yet remains cost efficient at
feature interaction learning, especially when coupled with a mixture of
low-rank architecture. DCN-V2 is simple, can be easily adopted as building
blocks, and has delivered significant offline accuracy and online business
metrics gains across many web-scale learning to rank systems at Google.

在大规模工业应用中，我们提出了改进版 DCN（DCN-V2）框架，以便更高效地学习特征交互。经广泛超参数搜索和模型调试等实验验证，改进版 DCN-V2 算法表现优于当今所有最新算法。该算法更具表现力，仍然具有成本效益，在低秩架构的混合耦合情况下，可用作构建块，并在 Google 的许多大规模学习排名系统中提高了离线精度和在线业务指标。