Understanding the Reparameterization Trick | by ML and DL Explained | Medium

The reparameterization trick is a crucial technique for training deep generative models such as Variational Autoencoders (VAEs). This post will explore the trick, its necessity, and how it aids in backpropagation through stochastic nodes. An encoder-decoder model receives an input sample (e.g., an image), compresses it into a lower-dimensional latent space ZZ, and then reconstructs the sample back to its original form. Unlike a standard encoder-decoder model, a VAE generates new samples rather than reconstructing inputs exactly. The encoder maps inputs to a latent space, but instead of a deterministic encoding, it learns a probabilistic distribution over the latent space. The decoder then samples from this distribution to generate new data points. A key difference is that in a VAE, we sample Z from a Gaussian distribution: This poses a problem during training because backpropagation requires differentiation, but sampling introduces a non-differentiable operation. To solve this issue, w

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• Lily Wang