This is an implementation of a deep residual network for predicting
the dynamics of 2D shapes as described in

  F. Fleuret. Predicting the dynamics of 2d objects with a deep
  residual network. CoRR, abs/1610.04032, 2016.

  https://arxiv.org/pdf/1610.04032v1

This package is composed of a simple 2d physics simulator called
'flatland' written in C++, to generate the data-set, and a deep
residual network 'dyncnn' written in the Lua/Torch7 framework.

You can run the reference experiment by executing the run.sh shell
script.

It will

  (1) generate the data-set of 50k triplets of images,

  (2) train the deep network, and output validation results every 100
      epochs. This take ~30h on a GTX 1080.

  (3) generate two pictures of the internal activations.

  (4) generate a graph with the loss curves if gnuplot is installed.

--
Francois Fleuret
Oct 21, 2016
Martigny