Generative Class-conditional Autoencoders
This work addresses the problem of efficient sampling from conditional distributions for researchers in generative modeling, but it is incremental as it builds on prior autoencoder methods.
The paper tackled the limitation of unimodal transition operators in denoising autoencoders by extending them to gated autoencoders, resulting in the generation of convincing class-conditional samples on MNIST and TFD datasets.
Recent work by Bengio et al. (2013) proposes a sampling procedure for denoising autoencoders which involves learning the transition operator of a Markov chain. The transition operator is typically unimodal, which limits its capacity to model complex data. In order to perform efficient sampling from conditional distributions, we extend this work, both theoretically and algorithmically, to gated autoencoders (Memisevic, 2013), The proposed model is able to generate convincing class-conditional samples when trained on both the MNIST and TFD datasets.