当适应新的任务τiτi,模型参数由参数θθ变为θ′iθi′,在MAML,新任务的模型参数为θ′iθi′通过一次或更多的梯度下降更新θ′i=θ−a▽θLτi(fθ)θi′=θ−a▽θLτi(fθ) 4 Meta-Imitation Learning with MAML 在本节中,我们将描述如何将模型无关的元学习算法(MAML)扩展
TL;DR 这篇文章作者将之前的一篇meta learning的算法(MAML)用到了Imitation learning上。 在MAML之上进行了修改(two head),使得,算法可以在测试的时候仅从视频demostration(没有action)模仿。 youtu.be/_9Ny2ghjwuY? 作者在corl上的talk。讲的比论文要清晰。(但不包含论文中4.1 4.2(two head)和5(bias transf...
One-Shot Visual Imitation Learning via Meta-Learning 通过元学习进行一站式视觉模仿学习 In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured environments. High-capacity ...
PMLR 2017 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Meta Learning 入门:MAML 和 Reptile fine tuning MAML toy example Motivation 学习一个网络初始化参数,能够快速泛化到新任务上...,根据梯度下降更新meta-learner的参数θ\thetaθ。 结束循环 Experiment 分析 MAML只是学习一个初始化参...
Visual navigation needs the agent locate the given target with visual perception. To enable robots to effectively execute tasks, combining large language m
所以,首先是这个模型具备meta属性,才使其能够做到one shot navigation,其次是其复杂的memory模块大幅度提升了记忆之前信息的能力,可以说通过一次的探索构建出了整个地图模型,从而能够提取信息到下一步的policy网络进行处理。大家可以看看这篇文章的演示视频,依然非常让人印象深刻。 3 Zero-Shot Imitation Learning 那么这...
COCO-Search18 fixation dataset for predicting goal-directed attention control Sci. Rep., 11 (1) (2021), pp. 1-11 Google Scholar Cornia et al., 2016 Cornia M., Baraldi L., Serra G., Cucchiara R. A deep multi-level network for saliency prediction 2016 23rd International Conference on Pa...
aged 1.13 to 5.56 years old, under naturalistic audio-visual conditions, i.e., when children are watching a popular cartoon adapted to their age. The goal is to compare the quality of the neural encoding/decoding of dynamic auditory and visual stimuli and audio-visual temporal coordination acros...
Increasing evidence suggests that early motor impairments are a common feature of autism. Thus, scalable, quantitative methods for measuring motor behavior in young autistic children are needed. This work presents an engaging and scalable assessment of v
aged 1.13 to 5.56 years old, under naturalistic audio-visual conditions, i.e., when children are watching a popular cartoon adapted to their age. The goal is to compare the quality of the neural encoding/decoding of dynamic auditory and visual stimuli and audio-visual temporal coordination acros...