Tagger: Deep Unsupervised Perceptual Grouping
Authors: Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hao, Harri Valpola, Jürgen Schmidhuber
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on multi-digit classification of very cluttered images that require texture segmentation. Remarkably our method achieves improved classification performance over convolutional networks despite being fully connected, by making use of the grouping mechanism. Furthermore, we observe that our system greatly improves upon the semi-supervised result of a baseline Ladder network on our dataset. |
| Researcher Affiliation | Collaboration | The Curious AI Company {antti,mathias,hotloo,harri}@cai.fi *IDSIA {klaus,juergen}@idsia.ch |
| Pseudocode | Yes | Algorithm 1: Pseudocode for running Tagger on a single real-valued example x. |
| Open Source Code | Yes | The datasets and a Theano [33] reference implementation of Tagger are available at http://github.com/ Curious AI/tagger |
| Open Datasets | Yes | The datasets and a Theano [33] reference implementation of Tagger are available at http://github.com/ Curious AI/tagger |
| Dataset Splits | Yes | It consists of 60,000 (train) + 10,000 (test) binary images of size 20x20. (...) We use a 50k training set, 10k validation set, and 10k test set to report the results. |
| Hardware Specification | Yes | The models reported in this paper took approximately 3 and 11 hours in wall clock time on a single Nvidia Titan X GPU for Shapes and Texture MNIST2 datasets respectively. |
| Software Dependencies | No | The paper mentions 'a Theano [33] reference implementation of Tagger' but does not specify a version number for Theano or any other key software dependencies used for the experiments. |
| Experiment Setup | Yes | We train Tagger in an unsupervised manner by only showing the network the raw input example x, not ground truth masks or any class labels, using 4 groups and 3 iterations. We average the cost over iterations and use ADAM [14] for optimization. On the Shapes dataset we trained for 100 epochs with a bit-flip probability of 0.2, and on the Texture MNIST dataset for 200 epochs with a corruption-noise standard deviation of 0.2. |