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

报告摘要:迁移学习一直是机器学习领域的难点问题,其目标是在数据分布变化的条件下实现强泛化能力。经过长期探索,逐步缩小了迁移学习的泛化理论与学习算法之间的鸿沟,获得了更紧致的泛化界和更优的学习器。此次报告将按照发展历程介绍迁移学习的代表性泛化理论及学习算法,重点介绍我们的间隔泛化理论及其对抗学习算法,同时介绍我们关于迁移学习的最新进展,包括局部泛化理论、迁移推理中的概率校准和无监督迁移学习算法。

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最新论文

Reinforcement Learning (RL) is a key technique to address sequential decision-making problems and is crucial to realize advanced artificial intelligence. Recent years have witnessed remarkable progress in RL by virtue of the fast development of deep neural networks. Along with the promising prospects of RL in numerous domains, such as robotics and game-playing, transfer learning has arisen as an important technique to tackle various challenges faced by RL, by transferring knowledge from external expertise to accelerate the learning process. In this survey, we systematically investigate the recent progress of transfer learning approaches in the context of deep reinforcement learning. Specifically, we provide a framework for categorizing the state-of-the-art transfer learning approaches, under which we analyze their goals, methodologies, compatible RL backbones, and practical applications. We also draw connections between transfer learning and other relevant topics from the RL perspective and explore their potential challenges as well as open questions that await future research progress.

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