Deep Generative Modeling(DGM)

The main goal of the DGM to estimate the likelihood of each observation and to create new samples from the underlying distribution, in other words take as input training samples from some distribution and learn a model that represents the distribution.It is achieved by density estimation and sample generation. We will be having some data which has a probability distribution and when we give new data this generates samples with the reference of the input PDF. Variables that are not directly observed but rather inferred from other variables that are observed(true exploratory variables).In latent variable model, there are different types of models auto encoders, Variational Auto encoders(VAEs) and Generative adversarial networks(GANs) Foundational generative model which tries to build this latent Variable Representation by self encoding the inputs.Encoder,Learns mapping from the data X to low dimensional latent space Z. Decode - mapping back from latent space Z to reconstruct observation as X' Autoencoders focus on Bottleneck layer which helps to learn a compressed latent representation and Reconstruction loss which helps the latent representation to capture(or encode) as much "information" about the data as possible. In this loss function does not use any labels, more likely output minus input is the loss obtained L(x) x-x' ² For VAEs impose a stochastic or variational twist on this architecture helps to generate smoother representation of data.In this instead of learning the latent variable(z) directly for each variable the VAEs learns mean and variance associated with that latent variable.this With the help of regularization term we can avoid overfitting.Futher Unlike in Autoencoders, In VAEs we cannot directly back-propagate gradients through sampling layers.To achieve back-propagation we need to parameterise the sampling layers such that, it could be trained end to end.