Скачать или смотреть 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

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