Recent research has shown great progress on fine-grained entity typing. Most existing methods require pre-defining a set of types and training a multi-class classifier from a large labeled data set based on multi-level linguistic features. They are thus limited to certain domains, genres and languages. In this paper, we pro- pose a novel unsupervised entity typing framework by combin- ing symbolic and distributional semantics. We start from learn- ing general embeddings for each entity mention, compose the em- beddings of specific contexts using linguistic structures, link the mention to knowledge bases and learn its related knowledge rep- resentations. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representa- tions. This framework doesn’t rely on any annotated data, prede- fined typing schema, or hand-crafted features, therefore it can be quickly adapted to a new domain, genre and language. Further- more, it has great flexibility at incorporating linguistic structures (e.g., Abstract Meaning Representation (AMR), dependency rela- tions) to improve specific context representation. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.

WS-DAN的PyTorch实现 介绍 这是“先看好：用于细粒度”一文的PyTorch实现。 它还具有正式的TensorFlow实现 。 该代码的核心部分指的是正式版本，最后，性能几乎达到了本文所报告的结果。 环境 Ubuntu 16.04，GTX 1080 8G * 2，CUDA 8.0 使用Python = 3.6.5，PyTorch = 0.4.1，torchvison = 0.2.1等的Anaconda。 必要时，某些第三方依赖项可能会与pip或conda一起安装。 结果 数据集 ACC（此仓库） ACC提炼（此仓库） ACC（纸） CUB-200-2011 88.20 89.30 89.4 FGVC飞机 93.15 93.22 93.0 斯坦福汽车 94.13 94.43 94.5 斯坦福犬 86.03 86.46 92.2 您可以从下载预训练

无水印，数字版，英文第一版。 Distributed systems have become more fine-grained in the past 10 years, shifting from code-heavy monolithic applications to smaller, self-contained microservices. But developing these systems brings its own set of headaches. With lots of examples and practical advice, this book takes a holistic view of the topics that system architects and administrators must consider when building, managing, and evolving microservice architectures. Microservice technologies are moving quickly. Author Sam Newman provides you with a firm grounding in the concepts while diving into current solutions for modeling, integrating, testing, deploying, and monitoring your own autonomous services. You’ll follow a fictional company throughout the book to learn how building a microservice architecture affects a single domain. Discover how microservices allow you to align your system design with your organization’s goals Learn options for integrating a service with the rest of your system Take an incremental approach when splitting monolithic codebases Deploy individual microservices through continuous integration Examine the complexities of testing and monitoring distributed services Manage security with user-to-service and service-to-service models Understand the challenges of scaling microservice architectures

这几天刚好调研fine-grained这个领域，花两天时间近几年细粒度检测识别领域顶会论文，已经经过仔细筛选。

我们的 CVPR 2019 论文 Distilling Object Detectors with Fine-grained Feature Imitation 的实现 我们提出了一种基于锚点的对象检测模型的通用蒸馏方法，以利用大型教师模型的知识获得增强的小型学生模型，该模型是正交的，可以进一步与量化和剪枝等其他模型压缩方法相结合。 香草知识蒸馏技术的关键观察是预测置信度的类间差异揭示了笨拙的模型如何趋于泛化（例如，当输入实际上是一只狗时，模型将在猫标签上放置多少置信度）。 虽然我们的想法是物体附近特征响应的位置间差异也揭示了检测器倾向于泛化的程度（例如，模型的响应对于不同的近物体锚点位置有何不同）。 我们发布了基于 shufflenet 的检测器和基于VGG11的Faster R-CNN 的提取代码，该代码库实现了基于Faster R-CNN模仿。 检查以获取基于 Shufflene

FIRM An Intelligent Fine-grained Resource Management Framework

Coupled Generative Adversarial Network for Continuous Fine-grained Action Segmentation

细粒度的自我监督学习 此存储库具有与用于细粒度图像分类的自我监督学习相关的代码。 我使用了木薯植物病数据集

计算机视觉Github开源论文

A Platform for Fine-Grained Resource Sharing in the Data Center 数据中心中细粒度资源共享的平台 1 Introduction 2 Target Environment 3 Architecture 4 Mesos Behavior 5 Implementation 6 Evaluation 7 Related Work 8 Conclusion