(Implemented using PyTorch)
This is an implementation of the TD-VAE introduced in this ICLR 2019 paper. TD-VAE is designed to have the following three features:
- It learns a compressed state representation of observations and makes predictions on the state level.
- Based on observations, it learns a belief state that contains all the information required to make predictions about the future.
- It learns to make predictions multiple steps in the future (jumpy predictions) directly instead of making predictions step by step.
Here, based on the information disclosed in the paper, we reproduce the experiment about moving MNIST digits. In this experiment, a sequence of a MNIST digit moving to the left or the right direction is presented to the model. The model predicts how the digit moves in subsequent steps. After training the model, we can feed a sequence of digits into the model and see how well it can predict the future.
Here is the result.
