PredCNN: Predictive Learning with Cascade Convolutions
Authors: Ziru Xu, Yunbo Wang, Mingsheng Long, Jianmin Wang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on the standard Moving MNIST dataset and two challenging crowd flow datasets, and show that Pred CNN outperforms the state-of-the-art recurrent models for video prediction on the standard Moving MNIST dataset and two challenging crowd flow prediction datasets, and achieves a faster training speed and lower memory footprint. |
| Researcher Affiliation | Academia | Ziru Xu , Yunbo Wang , Mingsheng Long , and Jianmin Wang KLiss MOE, School of Software, Tsinghua University, China National Engineering Laboratory for Big Data Software Beijing Key Laboratory for Industrial Big Data System and Application {xzr16,wangyb15}@mails.tsinghua.edu.cn, {mingsheng,jimwang}@tsinghua.edu.cn |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as "Pseudocode" or "Algorithm". |
| Open Source Code | Yes | Datasets and codes will be released at https://github.com/thuml. |
| Open Datasets | Yes | Taxi BJ and Bike NYC [Zhang et al., 2017] are two crowd flow prediction datasets, collected from GPS trajectory monitors in Beijing and New York respectively. ... Besides, we also apply our method to a commonly used video prediction dataset, Moving MNIST... |
| Dataset Splits | No | The paper specifies train and test splits for the datasets (e.g., "training set of 19, 788 sequences and a test set of 1, 344 sequences" for Taxi BJ), but it does not explicitly mention a separate "validation" dataset split. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU or CPU models used for running the experiments. It only mentions training time and memory usage. |
| Software Dependencies | No | All experiments are implemented in Keras [Chollet and others, 2015] with Tensor Flow [Abadi et al., 2016] as back-ends. The paper mentions software used but does not provide specific version numbers for Keras or TensorFlow. |
| Experiment Setup | Yes | Unless otherwise specified, the starting learning rate of Adam is set to 10 4, and the training process is stopped after 100 epochs with a batch size of 16. ... We use Adam optimizer with a starting learning rate of 10 4, 8 video sequences per batch and the training process stopped after approximately 200, 000 iterations. |