This repository is a Torch version of Building Autoencoders in Keras, but only containing code for reference - please refer to the original blog post for an explanation of autoencoders. Training hyperparameters have not been adjusted. The following models are implemented:
- AE: Fully-connected autoencoder
- SparseAE: Sparse autoencoder
- DeepAE: Deep (fully-connected) autoencoder
- ConvAE: Convolutional autoencoder
- UpconvAE: Upconvolutional autoencoder - also known by several other names (bonus)
- DenoisingAE: Denoising (convolutional) autoencoder [1, 2]
- CAE: Contractive autoencoder (bonus) [3]
- Seq2SeqAE: Sequence-to-sequence autoencoder
- VAE: Variational autoencoder [4, 5]
- CatVAE: Categorical variational autoencoder (bonus) [6, 7]
- AAE: Adversarial autoencoder (bonus) [8]
- WTA-AE: Winner-take-all autoencoder (bonus) [9]
Different models can be chosen using th main.lua -model <modelName>.
The denoising criterion can be used to replace the standard (autoencoder) reconstruction criterion by using the denoising flag. For example, a denoising AAE (DAAE) [10] can be set up using th main.lua -model AAE -denoising. The corruption process is additive Gaussian noise *~ N(0, 0.5)*.
MCMC sampling [10] can be used for VAEs, CatVAEs and AAEs with th main.lua -model <modelName> -mcmc <steps>. To see the effects of MCMC sampling with this simple setup it is best to choose a large standard deviation, e.g. -sampleStd 5, for the Gaussian distribution to draw the initial samples from.
The following luarocks packages are required:
- mnist
- dpnn (for DenoisingAE)
- rnn (for Seq2SeqAE)
If you find this library useful and would like to cite it, the following would be appropriate:
@misc{Autoencoders,
author = {Arulkumaran, Kai},
title = {Kaixhin/Autoencoders},
url = {https://github.com/Kaixhin/Autoencoders},
year = {2016}
}
[1] Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P. A. (2008, July). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning (pp. 1096-1103). ACM.
[2] Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P. A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11(Dec), 3371-3408.
[3] Rifai, S., Vincent, P., Muller, X., Glorot, X., & Bengio, Y. (2011). Contractive auto-encoders: Explicit invariance during feature extraction. In Proceedings of the 28th international conference on machine learning (ICML-11) (pp. 833-840).
[4] Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
[5] Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic Backpropagation and Approximate Inference in Deep Generative Models. In Proceedings of The 31st International Conference on Machine Learning (pp. 1278-1286).
[6] Jang, E., Gu, S., & Poole, B. (2016). Categorical Reparameterization with Gumbel-Softmax. arXiv preprint arXiv:1611.01144.
[7] Maddison, C. J., Mnih, A., & Teh, Y. W. (2016). The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. arXiv preprint arXiv:1611.00712.
[8] Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., & Frey, B. (2015). Adversarial autoencoders. arXiv preprint arXiv:1511.05644.
[9] Makhzani, A., & Frey, B. J. (2015). Winner-take-all autoencoders. In Advances in Neural Information Processing Systems (pp. 2791-2799).
[10] Arulkumaran, K., Creswell, A., & Bharath, A. A. (2016). Improving Sampling from Generative Autoencoders with Markov Chains. arXiv preprint arXiv:1610.09296.