Tagger: Deep Unsupervised Perceptual Grouping

We present a framework called TAG (iTerative Amortized Grouping) for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features. Tagger is the Ladder network wrapped in the TAG framework. This is the source code to replicate all experiments in the Tagger paper! Tagger is developed by The Curious AI Company with collaborations from Klaus Greff and Jürgen Schmidhuber, Swiss AI Lab IDSIA. The full length paper can be found at arXiv:1606.06724. Please use the following information for citation,

@misc{1606.06724,
   Author = {K. Greff and A. Rasmus and M. Berglund and T. Hao and J. Schmidhuber and H. Valpola},
   Title = {Tagger: Deep Unsupervised Perceptual Grouping},
   Year = {2016},
   Eprint = {arXiv:1606.06724},
}

Installation

Requirements

Linux or MacOS (fully tested on Ubuntu 14.04). No Windows support at the moment.
Anaconda

Setup (you only need to do it once):

bash ./install.sh

This downloads the dataset and pre-trained network parameters, installs submodules, and creates the Anaconda environment. After the installations, please run

source activate tagger

Experiments

The experiments train a newborn network to do perceptual grouping. The MNIST experiments also learn to perform classification.

Shapes50k20x20

To run the shapes50k20x20 experiment, please run

python runner-shapes50k20x20.py

Freq20-2MNIST and Freq20-1MNIST

Freq20-2MNIST experiment has two stages. Stage 1 is about unsupervised learning of perceptual grouping, where we turn off the supervised learning cost. In stage 2, the trained model in stage 1 is loaded with the addition of new randomly initialized layers on top. Here the supervised signals are turned on and we continue to train the network for classification tasks.

For stage 1 of the experiment, please run

python runner-freq20-2mnist.py --pretrain

The hyperparameters and trained parameters are stored in the results folder. It has all the necessary data to re-load the model. For stage 2, please run

python runner-freq20-2mnist.py --continue LOAD_FROM

where LOAD_FROM is the path of the stage 1 training results.

Visualizing pre-trained models

The training done in the experiments above could run for several hours on a modern GPU. Therefore, pre-trained networks are provided in the pretrained_models folder.

The following visualizations are created using the pre-trained models and publication_visualization.ipynb. For figure explanations, please check out the paper.

Network Visualization on Shapes50k20x20 dataset	Network Visualization on Freq20-2MNIST dataset

Datasets

The dataset generation scripts can be found in the data folder. To the best of our knowledge, the shapes dataset was first used in Reichert, D. P., Series, P, and Storkey, A. J. A hierarchical generative model of recurrent object-based attention in the visual cortex. In ICANN, pp. 18–25. Springer, 2011. The Freq20-2MNIST and Freq20-1MNIST datasets are created by us. If you use it, please cite our paper, much appreciated!

Shapes50k20x20 generation

Please run data/shapes.py. This should generate the shapes dataset. The pre-generated dataset (downloaded by install.sh) can also be downloaded from here

Freq20-2MNIST and Freq20-1MNIST generation

Please run the notebook data/FreqMNIST28.ipynb. The notebook contains all the necessary steps to generate the dataset. The pre-generated dataset (downloaded by install.sh) can also be downloaded from here and here

Discussion & Contribution

As a demonstrative source code release for our research, this repository will not be further developed, except for minor compatibility and bug fixes. However, discussions are always more than welcome! Please open an issue on the github page when you have anything in mind.

If you encounter issues understanding the structures of the algorithm, please take a look at the paper. The code is designed to follow the algorithm devised in the paper.
If you encounter issues understanding the monitoring of the experiments, please take a look at Blocks & Fuel. If it's not yet clear, please create an issue.
Even though we have provided the environment.yml for the execution environment replication, it will not be bullet proof. Please try your best to create an isolated environment for the experiments, hence minimizing issues from external dependencies.
Tagger uses Ladder as a parametric mapping. If your questions or problems are related to Ladder Networks, please go to that repo.

License (3-clause BSD)

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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