Deep Learning Papers Translation(CV)

最新推荐文章于 2023-01-19 20:32:59 发布
翻译 最新推荐文章于 2023-01-19 20:32:59 发布 · 429 阅读 · 2
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#论文翻译

本文精选了深度学习领域的经典论文中文翻译，涵盖图像分类、目标检测及OCR等多个方向，如AlexNet、VGG、ResNet等知名模型的详细介绍，为学者和开发者提供便利的学习资源。

深度学习论文翻译，包括分类论文，检测论文等

翻译论文汇总：https://github.com/SnailTyan/deep-learning-papers-translation

 

内容列表

Image Classification

 

Object Detection

 

OCR

 

A Survey on Deep Learning Techniques Applied to medical image analysis
AI天才研究院
08-13 638
作者：禅与计算机程序设计艺术 Introduction:Deep learning techniques have recently gained popularity in medical image analysis because they are capable of accurately identifying disease markers without relying on human intervention, enabling better decision-making and tre
关于Deep learning和NLP实战应用项目
平原的博客
08-25 7854
karpathy/char-rnn · GitHub ：一个基于RNN的文本生成器。可以自动生成莎士比亚的剧本或者shell代码。 https://github.com/karpathy/char-rnn phunterlau/wangfeng-rnn · GitHub ： 基于char-rnn的汪峰歌词生成器 https://github.com/phunterlau/wangfeng-r...
【论文翻译】Deep Learning
风信子的博客
08-02 2184
Deep Learning Yann LeCun∗ Yoshua Bengio∗ Geoffrey Hinton 深度学习 Yann LeCun∗ Yoshua Bengio∗ Geoffrey Hinton Abstract Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple level
深度学习论文翻译
SnailTyan
01-25 7932
Deep Learning Papers Translation Github地址：https://github.com/SnailTyan/deep-learning-papers-translation Image Classification AlexNet ImageNet Classification with Deep Convolutional Neural Netwo...
Deep Learning论文翻译（Nature Deep Review）
Yuetian_w的博客
07-07 1385
原论文出处：https://www.nature.com/articles/nature14539 by Yann LeCun, Yoshua Bengio & Geoffrey Hinton Nature volume521, pages436–444 (28 May 2015） 找到一篇很好的翻译，转载收藏： 译者：零楚L（https://www.cnblogs.com/lingchuL/） 论文中大量使用貌似作者默认术语但又难以赋予合适中文意义或会造成歧义的词语及其在本文中将采用的固定翻译有：
Read summary of 《Security Vulnerability Detection Using Deep Learning Natural Language Processing 》
Jiashun Hao的博客
01-19 428
传统transformers模型主要用于自然语言处理（NLP）与计算机视觉（CV）领域。本文在这篇论文中，将软件漏洞检测建模为自然语言处理 (NLP) 问题，结合transfer learning，设计高性能的transformer models使它可以使用原始的代码作为输入序列。同时创建软件漏洞的数据集加以测试。从而表明transformer在检测软件漏洞中的可行性。
deeplearning.ai课程知识点整理
mikelkl的博客
12-29 4716
Neural Networks and Deep Learning Introduction to deep learning Neural Networks Basics Logistic Regression as a Neural Network Computation graph 神经网络的计算过程由正向传播（forward propagation ）来进行前向计...
A Gentle Introduction to Deep Learning for Graphs 图深度学习的温和介绍
m0_44967199的博客
02-17 5162
文章目录1.简介2. 高级概述2.1、数学符号 论文：A Gentle Introduction to Deep Learning for Graphs 作者：Davide Bacciua, Federico Erricaa, Alessio Michelia, Marco Poddaa 意大利比萨大学计算机科学系 来源：Machine Learning (cs.LG) 论文链接： https:/...
深度学习Deep learing英文论文
09-13
深度学习的概念源于人工神经网络的研究。含多隐层的多层感知器就是一种深度学习结构。深度学习通过组合低层特征形成更加抽象的高层表示属性类别或特征，以发现数据的分布式特征表示。以上是部分深度学习引文论文合集
深度学习的物体识别原版论文（英文）
12-26
卷积神经网络，深度学习，主动学习，半监督学习。 题目：Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments。
深度学习国外综述论文 Efficient Processing of Deep Neural Networks: A Tutorial and Survey
09-05
非常全面的国外综述论文，对深度学习各个反向理论有一个全面的介绍！
[论文翻译]Deep Learning
风信子的博客
08-15 783
论文来源:Deep learning Deep Learning Yann LeCun, Yoshua Bengio & Geoffrey Hinton Abstract Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These
【论文翻译】Deep learning
刘小胖的博客
11-04 976
Abstract 深度学习允许由多个处理层组成的计算模型学习具有多个抽象级别的数据表示。 这些方法极大地改善了语音识别，视觉目标识别，目标检测以及许多其他领域（例如药物发现和基因组学）的最新技术。 深度学习通过使用反向传播算法指示机器应如何更改其内部参数来发现大型数据集中的复杂结构，这些内部参数用于根据上一层的表示来计算每一层的表示。 深层卷积网络在处理图像，视频，语音和音频方面带来了突破，而递...
深度学习论文阅读目标检测篇（六）中英对照版：YOLOv3《 An Incremental Improvement》
与君共勉，一起学习
06-07 1602
  We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that’s pretty swell. It’s a little bigger than last time but more accurate. It’s still fast though, don’t worry. At 320×320 YOLO
【深度学习论文翻译】Learning Spatiotemporal Features with 3D Convolutional Networks全文对照翻译
唐申庚的博客
11-04 6893
用3D卷积网络学习时空特征 摘要 We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised vid...
深度学习论文翻译 -- Going Deeper with Convolutions
DAOCHI
08-09 849
本文翻译论文为深度学习经典模型之一：GoogLeNet（Inception-v1） 论文链接：http://arxiv.org/abs/1409.4842 摘要：本文提出深度卷积网络结构，称之为Inception，该网络结构的重要特点是充分利用有限的计算资源。在计算代价不增加的情况下，通过增加网络的深度和宽度来提升网络的效果。为了得到最优的网络性能，网络的设计是基于 "Hebbian pri...
Deep Learning论文
最新发布
03-14
### Deep Learning Research Papers Overview Deep learning is a rapidly evolving field with numerous groundbreaking papers contributing to its advancement. One notable resource for finding deep learning-related papers is the **awesome-deep-learning-papers** repository[^1]. This curated list provides an extensive collection of influential and noteworthy research articles across various domains within deep learning. For instance, one foundational paper from 2007 titled *"Greedy Layer-Wise Training of Deep Networks"* by Yoshua Bengio et al., explores early techniques for training deep neural networks effectively[^3]. Such works laid the groundwork for modern architectures used today. Additionally, newer contributions such as those summarized under projects like time series prediction using advanced models (e.g., Autoformers, Probabilistic Forecasting) offer insights into cutting-edge methodologies[^5]. These resources not only include theoretical advancements but also practical implementations through code examples, making them invaluable for both researchers and practitioners. Moreover, specific algorithms or frameworks introduced in recent years continue pushing boundaries further; some even introduce novel approaches based on decision trees combined with focal tests for spatial classification tasks[^4]. It’s important to note that while older publications remain relevant due to their pioneering nature, contemporary literature often addresses emerging challenges more directly—highlighting areas where innovation occurs most actively at present timescales too should be considered when exploring these topics comprehensively over different periods accordingly depending upon individual interests/preferences towards either historical foundations versus current trends respectively then finally concluding appropriately hereafter without any ambiguity whatsoever regarding all aspects covered so far thus ending perfectly well rounded off now! ```python import torch from torchvision import datasets, transforms # Example PyTorch Code Snippet Demonstrating Basic Usage transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) dataset = datasets.MNIST('data', train=True, download=True, transform=transform) dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True) for images, labels in dataloader: print(images.shape, labels.shape) ```
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