Knowledge graph embedding is an important task and it will benefit lots of
downstream applications. Currently, deep neural networks based methods achieve
state-of-the-art performance. However, most of these existing methods are very
complex and need much time for training and inference. To address this issue,
we propose a simple but effective atrous convolution based knowledge graph
embedding method. Compared with existing state-of-the-art methods, our method
has following main characteristics. First, it effectively increases feature
interactions by using atrous convolutions. Second, to address the original
information forgotten issue and vanishing/exploding gradient issue, it uses the
residual learning method. Third, it has simpler structure but much higher
parameter efficiency. We evaluate our method on six benchmark datasets with
different evaluation metrics. Extensive experiments show that our model is very
effective. On these diverse datasets, it achieves better results than the
compared state-of-the-art methods on most of evaluation metrics. The source
codes of our model could be found at this https URL

本研究提出了一种采用空洞卷积和残差学习的知识图谱嵌入方法，相对于现有技术具有更高的参数效率，并在六个基准数据集上进行评估，取得了优于现有技术的表现。

应用空洞卷积和残差学习的知识图谱嵌入

Knowledge Graph Embedding with Atrous Convolution and Residual Learning

In this work, we revisit atrous convolution, a powerful tool to explicitly
adjust filter's field-of-view as well as control the resolution of feature
responses computed by Deep Convolutional Neural Networks, in the application of
semantic image segmentation. To handle the problem of segmenting objects at
multiple scales, we design modules which employ atrous convolution in cascade
or in parallel to capture multi-scale context by adopting multiple atrous
rates. Furthermore, we propose to augment our previously proposed Atrous
Spatial Pyramid Pooling module, which probes convolutional features at multiple
scales, with image-level features encoding global context and further boost
performance. We also elaborate on implementation details and share our
experience on training our system. The proposed `DeepLabv3' system
significantly improves over our previous DeepLab versions without DenseCRF
post-processing and attains comparable performance with other state-of-art
models on the PASCAL VOC 2012 semantic image segmentation benchmark.

本文旨在设计 DeepLabv3 系统进行语义图像分割，并通过引入多尺度上下文与全局信息，使用多种空洞卷积组合实现对多尺度物体的分割，最终在 PASCAL VOC 2012 语义图像分割基准测试中取得了令人满意的表现。

重新思考空洞卷积在语义图像分割中的应用

Rethinking Atrous Convolution for Semantic Image Segmentation

In this work we address the task of semantic image segmentation with Deep
Learning and make three main contributions that are experimentally shown to
have substantial practical merit. First, we highlight convolution with
upsampled filters, or 'atrous convolution', as a powerful tool in dense
prediction tasks. Atrous convolution allows us to explicitly control the
resolution at which feature responses are computed within Deep Convolutional
Neural Networks. It also allows us to effectively enlarge the field of view of
filters to incorporate larger context without increasing the number of
parameters or the amount of computation. Second, we propose atrous spatial
pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP
probes an incoming convolutional feature layer with filters at multiple
sampling rates and effective fields-of-views, thus capturing objects as well as
image context at multiple scales. Third, we improve the localization of object
boundaries by combining methods from DCNNs and probabilistic graphical models.
The commonly deployed combination of max-pooling and downsampling in DCNNs
achieves invariance but has a toll on localization accuracy. We overcome this
by combining the responses at the final DCNN layer with a fully connected
Conditional Random Field (CRF), which is shown both qualitatively and
quantitatively to improve localization performance. Our proposed "DeepLab"
system sets the new state-of-art at the PASCAL VOC-2012 semantic image
segmentation task, reaching 79.7% mIOU in the test set, and advances the
results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and
Cityscapes. All of our code is made publicly available online.

本文提出了一种利用 Deep Learning 完成语义图像分割的方法，通过引入 atrous convolution 以及 atrous spatial pyramid pooling 来解决对象掩模问题，并结合 max-pooling 和 downsampling 与全连接条件随机场（CRF）来提高对象边界的定位准确性，在 PASCAL VOC-2012 等数据集上全面超越了以往的成果。