行人检测是视频监控中的一个基本问题，近年来已经取得了长足的进步。 然而，由于源训练样本和目标场景中行人样本之间的差异，在某些公共数据集上训练的通用行人检测器的性能在应用于某些特定场景时会明显下降。 另外，在目标场景中手动标记样本也是一项昂贵且费时的工作。 我们提出了一种新颖的转移学习框架，该框架可以自动将通用检测器转移到特定于场景的行人检测器，而无需手动标记目标场景中的训练样本。 在我们的方法中，我们通过对目标场景使用通用检测器来获得初始检测结果，我们将该结果称为目标样本。 我们使用了几种线索来过滤目标模板，从最初的检测结果中我们可以确定它们的标签。 高斯混合模型（GMM）用于获取每个视频帧中的运动区域和一些其他目标样本，这些目标样本无法被通用检测器检测到，因为这些目标样本距离摄像机较远。 目标样本和目标模板之间的相关性以及源样本和目标模板之间的相关性通过稀疏编码进行估算，然后用于计算源样本和目标样本的权重。 显着性检测是在源样本和目标模板之间进行相关性计算以消除非显着区域干扰之前的一项必不可少的工作。 所有这些考虑都是在单个目标函数下制定的，通过对所有这些样本添加基于稀疏编码的权重来

Active control of near-field radiative heat transfer between graphene-covered metamaterials

Cross-Project Transfer Representation Learning for Vulnerable Function Discovery

DTW

Yang-2017-Chirality-and-energy-transfer-ampli.pdf

Code for multiple images in style transfer based on NEURAL TRANSFER USING PYTORCH 风格迁移中将多个图片同时转化。

SEMI E37-0298 HIGH-SPEED SECS MESSAGE SERVICES (HSMS) GENERIC.pdf

在胶囊网络上使用迁移学习完成方面级情感分类，用文档级的知识迁移到方面级上，资源提供论文翻译。原文可自己下载

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to compute

这是论文Visual Attribute Transfer through Deep Image Analogy的原文，内含有小编本人的理解与注释，谨以此分享个人经验和向广大大牛请教，如有错误，请不吝指出，万分感谢！