The reparameterization trick: How VAEs Backpropagate. скачать в хорошем качестве

The reparameterization trick: How VAEs Backpropagate.

The reparameterization trick is a clever technique used in Variational Autoencoders (VAEs) to make the training process possible using standardDecoder gradient-based optimization. Normally, VAEs learn to generate data by first learning a distribution (like a Gaussian) from which to sample, but sampling from this distribution means we can't backpropagate the loss to update the model (we can't differentiate a sampling process!). The reparameterization trick solves this by expressing the random sampling as a deterministic function: instead of sampling directly from the learned distribution, we sample from a fixed distribution (like a standard normal) and transform it using the learned parameters (mean and standard deviation). This allows gradients to flow through the network and makes the whole system trainable end-to-end. C: Deepia Join our Al community for more posts like this ‪@Giffah_Alexander‬ #deeplearning #neuralnetworks #mathematics #math #physics #computerscience #coding #science #education #machinelearning #datascience