Learning What and Where to Transfer 概述 这是一篇来自 ICML 2019 的迁移学习论文。作者针对异构师生网络的知识迁移任务,提出了一种基于元学习的迁移学习方法,自动地学习源网络中什么知识需要迁移、迁移到目标网络的什么地方。也就是说,通过元学习来决定: (a)源
文章标题:Learning What and Where to Transfer (Accepted for publication at ICML 2019) 文章链接:arxiv.org/abs/1905.0590 代码链接:github.com/alinlab/L2T- 本文小结:使用 meta-networks 在异构网络之间完成更加准确有效的知识迁移,即在目标模型和源模型之间,哪些层对的哪些特征进行多大程度的知识迁移。通过实验...
Learning What and Where to Transfer(ICML 2019)中提出基于meta-learning的迁移学习方法,利用meta-learning学习什么样的迁移策略能够达到最优效果。首先,本文的迁移方法采用了FITNETS: HINTS FOR THIN DEEP NETS(ICLR 2015)提出的思路,在finetune阶段通过对target模型参数和pretrain模型参数添加L2正则化损失,来控制target...
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What to transfer First and foremost, step in thetransfer learningprocess is to identify the parts of the knowledge we seek to transfer from source to target and is common between them to improve the performance and save re-building/training time. ...
An Interactive Approach to Transfer Learning Using theDeep Network Designerapp, you caninteractively complete the entire transfer learning workflow—including selecting or importing (from MATLAB, TensorFlow, or PyTorch) a pretrained model, modifying the final layers, and retraining the network using new ...
目前在transfer RL有两个主要的研究思路,一是直接寻找对环境变化鲁棒的策略,二是尽可能有效地将策略从source domain迁移到target domain。本文提出的方法属于后者,目的是复用之前学到的策略或者知识来提高样本效率,从而减少智能体在target domain上的探索开销。目前的相关算法仍然需要在target domain做大量的探索和优化。
6Learning What and Where to Transfer (paper)ICML 2019meta-TLnew trend 5On Learning Invariant Representation for Domain Adaptation (paper)ICML 2019theory 4Do better ImageNet models transfer better? (paper)CVPR 2019transferabilitygood question
1.1 Difficulty of transfer Transfer of learning occurs when a set of skills acquired in one domain generalizes to other domains or improves general cognitive abilities. Transfer is an important question both theoretically and practically. Mestre (2005) distinguishes between near-transfer, where transfer...
As the application of deep learning has expanded to real-world problems with insufficient volume of training data, transfer learning recently has gained much attention as means of improving the performance in such small-data regime. However, when existing methods are applied between heterogeneous ...