七月流火

不积跬步,无已至千里; 不积小流,无以成江海。

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本文转自https://zybuluo.com/hanbingtao/note/541458,膜拜大神。大神的博客的图片访问不到了,原因是用了简书的图床,在早期的博客文章我也用过简书的图床,之后可能简书对访问图片的域名进行限制,导致访问不了,目前使用gitlab作为图床,也有被限制的风险。本文手动转自hanbingtao大神的博客,一来促使自己仔细阅读循环神经网络的细节,二来对大神博客的图片不能访问做修复,使其他人更方便阅读。三学习借鉴大神写作格式和风格。

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方向:一般来讲开展一个方向,在这个方向读 2,30 篇论文就够了,能够对这个方向有一个把握,然后选一个自己感兴趣的小方向,就是想 idea,这个时候你的 idea 是基于哪篇论文的,就把这篇论文复现,然后在这个基础上实验你的 idea,想发好的论文不是看得多或者是看的深,你要多做,多做实验,多跑实验,多想idea,尽可能快的验证你的 idea 的合理性,建议你不要再按照这个思路来,刚刚入坑的话要先关注量,有了量然后选一个小方向深挖,然后就是不断想 idea,实现 idea,验证 idea 的循环了。

综述,一般用原关键词再加上 review, mete analysis

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论文题目:EAST: An Efficient and Accurate Scene Text Detector

Abstract

Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple stages and components in the pipelines. In this work, we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes. The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images, eliminating unnecessary intermediate steps (e.g., candidate aggregation and word partitioning), with a single neural network. The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR 2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps at 720p resolution.

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论文题目:An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

Abstract

Image-based sequence recognition has been a longstanding research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed. Compared with previous systems for scene text recognition, the proposed architecture possesses four distinctive properties: (1) It is end-to-end trainable, in contrast to most of the existing algorithms whose components are separately trained and tuned. (2) It naturally handles sequences in arbitrary lengths, involving no character segmentation or horizontal scale normalization. (3) It is not confined to any predefined lexicon and achieves remarkable performances in both lexicon-free and lexicon-based scene text recognition tasks. (4) It generates an effective yet much smaller model, which is more practical for real-world application scenarios. The experiments on standard benchmarks, including the IIIT-5K, Street View Text and ICDAR datasets, demonstrate the superiority of the proposed algorithm over the prior arts. Moreover, the proposed algorithm performs well in the task of image-based music score recognition, which evidently verifies the generality of it.

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提纲:

  1. 哈希算法
  2. java中HashMap分析
  3. 分布式集群管理
  4. 一致性Hash算法

论文题目:IPFS - Content Addressed, Versioned, P2P File System

Abstract

The InterPlanetary File System (IPFS) is a peer-to-peer distributed file system that seeks to connect all computing devices with the same system of files. In some ways, IPFS is similar to the Web, but IPFS could be seen as a single BitTorrent swarm, exchanging objects within one Git repository. In other words, IPFS provides a high throughput content-addressed block storage model, with content-addressed hyper links. This forms a generalized Merkle DAG, a data structure upon which one can build versioned file systems, blockchains, and even a Permanent Web. IPFS combines a distributed hashtable, an incentivized block exchange, and a self-certifying namespace. IPFS has no single point of failure, and nodes do not need to trust each other.

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在学习机器学习和神经网络过程中,对于模型参数和模型计算量需要做出评估,这篇文章介绍几种常见操作对模型带来的参数量和计算量,更专业的表示为空间复杂度和时间复杂度,后续再补充。

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If you don’t believe me or don’t get it, I don’t have time to try to convince you 。