Learning to Decompose and Disentangle Representations for Video Prediction
Authors: Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F. Fei-Fei, Juan Carlos Niebles
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate DDPAE on two datasets: Moving MNIST [31] and Bouncing Balls [3].Our goal is to predict a sequence of future frames given a sequence of input frames. |
| Researcher Affiliation | Academia | Jun-Ting Hsieh Stanford University junting@stanford.eduBingbin Liu Stanford University bingbin@stanford.eduDe-An Huang Stanford University dahuang@cs.stanford.eduLi Fei-Fei Stanford University feifeili@cs.stanford.eduJuan Carlos Niebles Stanford University jniebles@cs.stanford.edu |
| Pseudocode | No | The paper describes the model implementation and learning process in prose and figures, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for DDPAE and the experiments are available at https://github.com/jthsieh/DDPAE-video-prediction. |
| Open Datasets | Yes | Moving MNIST has been widely used for evaluating video prediction models [15, 31, 43].The test set is a fixed dataset downloaded from [31] consisting of 10,000 sequences of 20 frames, with 10 as input and 10 to predict.We simulate sequences of 4 balls bouncing in an image with the physics engine code used in [3]. |
| Dataset Splits | No | The paper specifies training and test set sizes for Bouncing Balls ('We generated a fixed training set of 50,000 sequences and a test set of 2,000 sequences.') and describes the test set for Moving MNIST ('The test set is a fixed dataset downloaded from [31] consisting of 10,000 sequences of 20 frames, with 10 as input and 10 to predict.'), but it does not explicitly define a separate validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'physics engine code used in [3]' and 'seq2seq based model', but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | No | The paper describes some aspects of dataset generation (e.g., 'randomly sampled velocity and angle' for Moving MNIST, 'maximum velocity is 60 pixels/second' for Bouncing Balls), but it does not explicitly provide concrete hyperparameter values or detailed system-level training configurations for the model itself. |