迁移学习(Transfer Learning)是一种机器学习方法,是把一个领域(即源领域)的知识,迁移到另外一个领域(即目标领域),使得目标领域能够取得更好的学习效果。迁移学习(TL)是机器学习(ML)中的一个研究问题,着重于存储在解决一个问题时获得的知识并将其应用于另一个但相关的问题。例如,在学习识别汽车时获得的知识可以在尝试识别卡车时应用。尽管这两个领域之间的正式联系是有限的,但这一领域的研究与心理学文献关于学习转移的悠久历史有关。从实践的角度来看,为学习新任务而重用或转移先前学习的任务中的信息可能会显着提高强化学习代理的样本效率。

知识荟萃

迁移学习荟萃20191209

综述

理论

模型算法

多源迁移学习

异构迁移学习

在线迁移学习

小样本学习

深度迁移学习

多任务学习

强化迁移学习

迁移度量学习

终身迁移学习

相关资源

领域专家

课程

代码

数据集

  • MNIST vs MNIST-M vs SVHN vs Synth vs USPS: digit images
  • GTSRB vs Syn Signs : traffic sign recognition datasets, transfer between real and synthetic signs.
  • NYU Depth Dataset V2: labeled paired images taken with two different cameras (normal and depth)
  • CelebA: faces of celebrities, offering the possibility to perform gender or hair color translation for instance
  • Office-Caltech dataset: images of office objects from 10 common categories shared by the Office-31 and Caltech-256 datasets. There are in total four domains: Amazon, Webcam, DSLR and Caltech.
  • Cityscapes dataset: street scene photos (source) and their annoted version (target)
  • UnityEyes vs MPIIGaze: simulated vs real gaze images (eyes)
  • CycleGAN datasets: horse2zebra, apple2orange, cezanne2photo, monet2photo, ukiyoe2photo, vangogh2photo, summer2winter
  • pix2pix dataset: edges2handbags, edges2shoes, facade, maps
  • RaFD: facial images with 8 different emotions (anger, disgust, fear, happiness, sadness, surprise, contempt, and neutral). You can transfer a face from one emotion to another.
  • VisDA 2017 classification dataset: 12 categories of object images in 2 domains: 3D-models and real images.
  • Office-Home dataset: images of objects in 4 domains: art, clipart, product and real-world.
  • DukeMTMC-reid and Market-1501: two pedestrian datasets collected at different places. The evaluation metric is based on open-set image retrieval.
  • Amazon review benchmark dataset: sentiment analysis for four kinds (domains) of reviews: books, DVDs, electronics, kitchen
  • ECML/PKDD Spam Filtering: emails from 3 different inboxes, that can represent the 3 domains.
  • 20 Newsgroup: collection of newsgroup documents across 6 top categories and 20 subcategories. Subcategories can play the role of the domains, as describe in this article.

实战


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最近更新:2019-12-09

VIP内容

通过调整预训练的机器学习模型来解决特殊问题,在时间内建立自定义NLP模型。

在自然语言处理迁移学习中,您将学习:

  • 用新的领域数据对预训练的模型进行微调
  • 选择正确的模型来减少资源的使用
  • 用于神经网络结构的迁移学习
  • 生成文本与生成预先训练的Transformers
  • BERT跨语言迁移学习
  • 探索自然语言处理学术文献的基础

https://www.manning.com/books/transfer-learning-for-natural-language-processing

从头开始训练深度学习NLP模型是昂贵的、耗时的,并且需要大量的数据。在自然语言处理的迁移学习中,DARPA研究员Paul Azunre揭示了前沿的迁移学习技术,可以将可定制的预训练模型应用到您自己的NLP架构中。您将学习如何使用迁移学习为语言理解提供最先进的结果,即使使用有限的标签数据。最重要的是,您将节省训练时间和计算成本。

关于本书:

自然语言处理迁移学习教你通过构建现有的预训练模型快速创建强大的NLP解决方案。这是一本非常有用的书,书中提供了一些非常清晰的概念解释,你需要这些概念来学习转学,同时也提供了一些实际的例子,这样你就可以马上练习你的新技能。随着您的学习,您将应用最先进的迁移学习方法来创建垃圾邮件分类器、事实检查器和更多的现实世界的应用程序。

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最新论文

With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance. However, in regard to HSI classification, a large number of labeled samples is generally difficult to acquire due to the difficulty and time-consuming nature of manual labeling. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. In this article, we concentrate on this topic and provide a systematic review of the relevant literature. Specifically, the contributions of this paper are twofold. First, the research progress of related methods is categorized according to the learning paradigm, including transfer learning, active learning and few-shot learning. Second, a number of experiments with various state-of-the-art approaches has been carried out, and the results are summarized to reveal the potential research directions. More importantly, it is notable that although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model. For reproducibility, the source codes of the methods assessed in the paper can be found at https://github.com/ShuGuoJ/HSI-Classification.git.

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