Auxiliary Deep Generative Models
Authors: Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, Ole Winther
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show state-of-the-art performance within semi-supervised learning on MNIST, SVHN and NORB datasets. |
| Researcher Affiliation | Academia | 1Department of Applied Mathematics and Computer Science, Technical University of Denmark 2Bioinformatics Centre, Department of Biology, University of Copenhagen |
| Pseudocode | No | The paper describes models and training procedures mathematically and textually, but it does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Implementation is available in a repository named auxiliary-deep-generative-models on github.com. |
| Open Datasets | Yes | For the benchmark datasets we show (cf. Sec. 4.4): (iii) State-of-the-art results on several semi-supervised classification tasks. We demonstrate the benefits of the increased flexibility by achieving state-of-the-art performance in the semi-supervised setting for the MNIST (Le Cun et al., 1998), SVHN (Netzer et al., 2011) and NORB (Le Cun et al., 2004) datasets. |
| Dataset Splits | Yes | For the MNIST dataset we have combined the training set of 50000 examples with the validation set of 10000 examples. The test set remained as is. |
| Hardware Specification | Yes | This research was supported by the Novo Nordisk Foundation, Danish Innovation Foundation and the NVIDIA Corporation with the donation of TITAN X and Tesla K40 GPUs. |
| Software Dependencies | No | The models are implemented in Python using Theano (Bastien et al., 2012), Lasagne (Dieleman et al., 2015) and Parmesan libraries |
| Experiment Setup | Yes | For training, we have used the Adam (Kingma and Ba, 2014) optimization framework with a learning rate of 3e4, exponential decay rate for the 1st and 2nd moment at 0.9 and 0.999, respectively. The β constant was between 0.1 and 2 throughout the experiments. |