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
《小王爱迁移》系列之二十二:异构网络的迁移:Learn What-Where to Transfer
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...
(CoRL2020)DIRL: Domain-Invariant Representation Learning Approach for Sim-to-Real Transfer 论文笔记 本文针对的问题是无监督领域自适应和半监督领域自适应问题。 与传统的对抗领域自适应方法对比,其创新性在于 在对齐边缘概率分布的同时也对齐条件概率分布(虽然感觉现在大家都在对齐条件概率分布,应该不能算新颖了) ...
Stanford Dogs:http://vision.stanford.edu/aditya86/ImageNetDogs/ You need to run the below pre-processing script for DataLoader. python cub200.py /data/CUB_200_2011 python dog.py /data/dog Train L2T-ww You can train L2T-ww models with the same settings in our paper. ...
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
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。
The main idea behind transfer learning is to use what the model already knows from solving a task with labeled data and apply that knowledge to a new task that doesn’t have much data. Instead of starting from scratch, we begin with the patterns and information the model learned from a si...
最近的一种方法被称之为"Fix-before-transfer"，指的是我们首先对下游任务模型进行训练，之后再通过与预...
and success. Skills focus on the “what” in terms of the abilities a student needs to perform a specific task or activity. They don’t provide enough connection to the how. Competencies take this to the next level by translating skills into behaviors that demonstrate what has been learned...