We present a generative denoising autoencoder model that has an embedded data classifier in its architecture in order to take advantage of class-based discriminating features and produce better data samples. The proposed model is a conditional generative model and is sampled with a Markov chain Monte Carlo (MCMC) process according to a label that denotes the desired (or undesired) class or classes. In this sense, any chosen predefined class or characteristic may have a positive or negative effect on the image generation process, meaning that it can be instructed to be present or absent from the generated sample. We argue that allowing discriminative information in the form of feature detectors to be present in the latent representation of the autoencoder can be generally beneficial. This technique is an alternative approach to variational autoencoders (VAEs) that enforce a prior on the latent distribution. We further claim that supervised learning may be generally able to serve unsupervised learning through an interaction between the two paradigms. However, the extreme majority of research done on the interaction of the two learning regimes has the goal of using unsupervised learning to improve supervised learning. In this article, we explore the two learning paradigms' interaction in the opposite direction.