Click-Through Rate(CTR) estimation has become one of the most fundamental
tasks in many real-world applications and it's important for ranking models to
effectively capture complex high-order features. Shallow feed-forward network
is widely used in many state-of-the-art DNN models such as FNN, DeepFM and
xDeepFM to implicitly capture high-order feature interactions. However, some
research has proved that addictive feature interaction, particular feed-forward
neural networks, is inefficient in capturing common feature interaction. To
resolve this problem, we introduce specific multiplicative operation into DNN
ranking system by proposing instance-guided mask which performs element-wise
product both on the feature embedding and feed-forward layers guided by input
instance. We also turn the feed-forward layer in DNN model into a mixture of
addictive and multiplicative feature interactions by proposing MaskBlock in
this paper. MaskBlock combines the layer normalization, instance-guided mask,
and feed-forward layer and it is a basic building block to be used to design
new ranking model under various configurations. The model consisting of
MaskBlock is called MaskNet in this paper and two new MaskNet models are
proposed to show the effectiveness of MaskBlock as basic building block for
composing high performance ranking systems. The experiment results on three
real-world datasets demonstrate that our proposed MaskNet models outperform
state-of-the-art models such as DeepFM and xDeepFM significantly, which implies
MaskBlock is an effective basic building unit for composing new high
performance ranking systems.

本研究中，我们提出了一种名为 MaskNet 的新型深度神经网络模型，它采用一种实例引导掩码技术，有效地解决了在 CTR 估计中常见的特征交互问题，并展示了其在排名任务中的优越性，证明了实例引导掩码在构建高性能排名系统中的有效性。

MaskNet: 通过实例引导蒙版引入特征逐层乘积到 CTR 排名模型

MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models  by Instance-Guided Mask

We tackle the problem of one-shot segmentation: finding and segmenting a
previously unseen object in a cluttered scene based on a single instruction
example. We propose a novel dataset, which we call $\textit{cluttered
Omniglot}$. Using a baseline architecture combining a Siamese embedding for
detection with a U-net for segmentation we show that increasing levels of
clutter make the task progressively harder. Using oracle models with access to
various amounts of ground-truth information, we evaluate different aspects of
the problem and show that in this kind of visual search task, detection and
segmentation are two intertwined problems, the solution to each of which helps
solving the other. We therefore introduce $\textit{MaskNet}$, an improved model
that attends to multiple candidate locations, generates segmentation proposals
to mask out background clutter and selects among the segmented objects. Our
findings suggest that such image recognition models based on an iterative
refinement of object detection and foreground segmentation may provide a way to
deal with highly cluttered scenes.

本文提出了一种名为 $\textit {cluttered Omniglot}$ 的新数据集并使用基线结构来解决一次性分割问题，结果表明在这种视觉搜索任务中，检测和分割是相互交织的问题，解决其中一个问题有助于解决另一个问题。为此，我们引入了 $	extit {MaskNet}$，这是一种改进的模型，它关注多个候选位置，生成分割建议来遮挡背景杂物并在分割对象中进行选择。发现这种基于对象检测和前景分割的迭代优化的图像识别模型可能提供了一种处理高度混杂场景的方法。