[译]Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks,程序员大本营,技术文章内容聚合第一站。
GAN通常由相互竞争的两个网络组成:一个生成器和一个鉴别器。在生成器学习从输入数据生成现实样本的同时,鉴别器学习识别哪些样本是真实的以及生成了哪些样本,从而参与了两个玩家的最小-最大游戏. 生成器 生成器使用解码器LSTM构建。类似于条件GAN [37],我们的生成器以从多元正态分布中采样的噪声矢量z作为输入,并以...
【NeurIPS 2019】图神经网络(GNN)论文Social-BiGAT Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks.pdf 2019-09-15上传 暂无简介 文档格式: .pdf 文档大小: 1.48M 文档页数: 11页 顶/踩数: 0/0 收藏人数: 0 评论次数: ...
相比上一次的Helmet-GAN模型,这次修改后的模型具有更好的稳定性。 第一次的尝试: leaolx:Helmet-GAN这次修改名字为自行车头盔生成对抗网络(Bicycle Helmet GAN, BH-GAN)。 生成器的误差基本控制在了9%以内,生…
We explicitly account for the multimodal nature of the prediction problem by forming a reversible transformation between each scene and its latent noise vector, as in Bicycle-GAN. We show that our framework achieves state-of-the-art performance comparing it to several baselines on existing ...
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