图3 MovingMNIST数据集可视化效果 TrafficBJ和Human3.6数据集 我们列出详细的比较如下表所示,简单的SimVP仍然能够取得SOTA的效果。 KTH数据集 基于CNN的方法潜在的局限性是,它可能难以伸缩到具有复杂长度的预测。我们通过模仿RNN来处理这个问题,RNN将以前的预测作为最近的输入,反复产生长期的预测。如下表所示,SimVP仍然取...
5.3 Radar echo dataset 对于雷达数据集的最难的地方就在于它没有所谓的明显的周期性,并且移动的速度也是不固定的,变换也不是极具严格的,比如Moving MNIST dataset数据集运动的对象是数字,这个数字本身空间的信息基本上是不变的,这个和识别问题类似,而雷达数据集会因为各种天气原因,慢慢的积累、消散或变化,或者快速的...
在Moving Mnist上的表现 表现出最好结果 更多实验细节请自行读paper,最好是边复现边读,意义更大,效果更佳,这边不带着一起读了,因为前几篇都详细讲述过,差别不大。 五、Conclusions 把3D-Conv引入到LSTM的内部,并对普通的连接做了一些实验说明,也给出了简要的理由。 提出RECALL机制,里面用了一些attention的思想,...
We tested the proposed method on the following datasets: MNIST moving digits, the Mixamo human bodies motions [15], JPEG [5] and CWIPC-SXR [32] real-world dynamic bodies. Simulation results demonstrate that our method outperforms the current baseline methods given its improved ability to model...
In contrast to DRAW, Pixel RNNs use a distinctly un-human approach: they model the probability of raw pixel values. The goal of the work is to be able to model natural images on a large scale, but the authors also evaluated Pixel RNNs on good old MNIST, and reported the best result...
MNIST CNN(训练及预测命令行。预测图片需使用28*28大小的24位BMP格式,黑底白字): .\Release\OpenCLNet.exe MNIST_CNN /ds :mnist_folder D:\DataSets\MNIST\ .\Release\OpenCLNet.exe MNIST_CNN /p :params_file D:\DataSets\MNIST_CNN.clnetparams :file D:\9.bmp ...
We evaluate our proposal using the task of next video frame prediction and the Moving MNIST dataset. The proposed method requires 38% less multiplications and 21% less parameters compared to the fully convolutional counterpart. In price of the reduced computational complexity, the performance measured...
Repository files navigation README MIT license PredRNN Contains PyTorch implementation of paper- PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs link Datasets The Moving MNIST dataset can be downloaded here ArchitectureAbout...
The validation experiment on Moving-Mnist and KTH dataset demonstrates that SARNN can output more accurate and clearer prediction frames.Liang, YonghuiBeijing Institute of TechnologyZhang, LuNational Satellite Meteorological Center, China Meteorological, Administration (NSMC/CMA)He, Yuqing...
MNIST CNN(训练及预测命令行。预测图片需使用28*28大小的24位BMP格式,黑底白字): .\Release\OpenCLNet.exe MNIST_CNN /ds :mnist_folder D:\DataSets\MNIST\ .\Release\OpenCLNet.exe MNIST_CNN /p :params_file D:\DataSets\MNIST_CNN.clnetparams :file D:\9.bmp D:/DataSets/下需包含MNIST数据集...