【轨迹预测系列】【笔记】MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction Abstract Memory Augmented Neural Network.用RNN对过去和未来的轨迹进行编码,产生了额外的memory,存储下来,用于获取编码结果。然后用存下来的未来编码结果,结合历史信息进行轨迹的预测。采用了rasterized的地图进行CNN处理来理解场...
原文地址openaccess.thecvf.com/content_CVPR_2020/papers/Marchetti_MANTRA_Memory_Augmented_Networks_for_Multiple_Trajectory_Prediction_CVPR_2020_paper.pdf Marchetz/MANTRA-CVPR20github.com/Marchetz/MANTRA-CVPR20 1、前言以及背景 本文采用Memory Augmented Neural Network (MANN)来解决轨迹预测问题中的多...
本文是2016年ICML的会议论文,作者来自谷歌的DeepMind。在论文中作者提出了一种记忆增强神经网络(memory-augmented neural networks,简记MANN)来快速吸收样本中蕴含的信息并利用这些信息对仅提供数个样本的情境做出准确的预测,即少样本学习(Few-Shot Learning)。由于使用了外部记忆部件,因此作者还提出一种有效获取外部记忆部...
具有增强记忆能力的网络结构,例如NTMs具有快速编码新信息的能力,因此能消除传统模型的缺点。这里,我们证明了记忆增强神经网络(memory-augmented neural network)具有快速吸收新数据知识的能力,并且能利用这些吸收了的数据,在少量样本的基础上做出准确的预测。 我们也介绍了一个访问外部记忆存储器的方法,该方法关注于记忆存储...
我们从最近的 memory-augmented neural networks 以及 co-attention mechanism 得到启发,本文中,我们利用 memory-networks 来记忆 rare events,然后用 memory-augmented networks with attention to rare answers for VQA. 2. The Proposed Algorithm: 本文的算法流程如上图所示,首先利用 embedding 的方法,提取问题和图像...
Memory-augmented networkGenerative adversarial networkImage recoloringVehicle recoloringFew-shot learningDespite the notable successes of Generative adversarial networks (GANs) achieved to date, applying them to real-world problems still poses significant challenges. In real traffic surveillance scenarios, for ...
In this paper, we propose a cognitive memory-augmented network (CMAN) for visual anomaly detection, which combines cognitive computing and neural networks. The proposed CMAN method is based on a memory module and a density estimator, which is to learn features from observed data and to model ...
MemoryAugmentedControlNetworks内存增强控制网络 系统标签: memoryaugmentednetworks控制网络control内存 UnderreviewasaconferencepaperatICLR2018MEMORYAUGMENTEDCONTROLNETWORKSArbaazKhan,ClarkZhang,NikolayAtanasov,KonstantinosKarydis,VijayKumar,DanielD.LeeGRASPLaboratory,UniversityofPennsylvaniaABSTRACTPlanningproblemsinpartiallyobserv...
But, even memory networks with sophisticated addressing schemes are unable to learn intelligent reasoning satisfactorily due to the complexity of simultaneously learning to access memory and plan. To mitigate these challenges we introduce the Memory Augmented Control Network (MACN). The proposed network ...
Memory-augmented Learning 记忆驱动的智能学习