The authors presented a new generative model that learns to disentangle the factors of variations of the data. The authors claim that the proposed model is pretty robust to supervision. This is achieved by combining two of the most successful generative models: VAE and GAN. The model is able to resolve the analogies in a consistent way on several datasets with minimal parameter/architecture tunning. This paper presents a way to learn latent codes for data, that captures both the information relevant for a given classification task, as well as the remaining irrelevant factors of variation (rather than discarding the latter as a classification model would). This is done by combining a VAE-style generative model, and adversarial training. This model proves capable of disentangling style and content in images (without explicit supervision for style information), and proves useful for analogy resolution. This paper introduces a generative model for learning to disentangle hidden factors of variation. The disentangling separates the code into two, where one is claimed to be the code that descries factors relevant to solving a specific task, and the other describing the remaining factors. Experimental results show that the proposed method is promising. The authors combine state of the art methods VAE and GAN to generate images with two complementary codes: one relevant and one irrelevant. They major contribution of the paper is the development of a training procedure that exploits triplets of images (two sharing the relevant code, one note sharing) to regularize the encoder-decoder architecture and avoid trivial solutions. The results are qualitatively good and comparable to previous article using more sources of supervision. Paper seeks to explore the variations amongst samples which separate multiple classes using auto encoders and decoders. Specifically, the authors propose combining generative adversarial networks and variational auto encoders. The idea mimics the game play between two opponents, where one attempts to fool the other into believing a synthetic sample is in fact a natural sample. The paper proposes an iterative training procedure where a generative model was first trained on a number of samples while keeping the weights of the adversary constant and later the adversary is trained while keeping the generative model weights constant. The paper performs experiments on generation of instances between classes, retrieval of instances belonging to a given class, and interpolation of instances between two classes. The experiments were performed on MNIST, a set of 2D character animation sprites, and 2D NORB toy image dataset.