A variational autoencoder is a generative model with a prior and noise distribution respectively. Usually such models are trained using the expectation-maximization meta-algorithm (e.g. probabilistic PCA, (spike & slab) sparse coding).

Mar 4, 2025 · Variational Autoencoders (VAEs) are generative models in machine learning (ML) that create new data similar to the input they are trained on. Along with data generation they also perform common autoencoder tasks like denoising. Like all autoencoders VAEs consist of: Encoder: Learns important patterns (latent variables) from input data.

Aug 13, 2024 · As machine learning technology advances at an unprecedented pace, Variational Autoencoders (VAEs) are revolutionizing the way we process and generate data. By merging powerful data encoding with innovative generative capabilities, VAEs offer transformative solutions to complex challenges in the field.

Apr 3, 2017 · The objetive of an autoencoder is to learn an encoding of something (along with its decoding function). There are many uses for an encoding. In a variational autoencoder what is learnt is the distribution of the encodings instead of the encoding function directly.

Jun 12, 2024 · Variational autoencoders (VAEs) are generative models used in machine learning to generate new data samples as variations of the input data they’re trained on.

Oct 4, 2024 · The objective of an autoencoder is to minimize the difference between the input and the output, typically measured using a loss function like Mean Squared Error. What Makes VAEs...

Sep 14, 2018 · The VAE training objective is to maximize $P(x)$. We’ll model $P(x|z)$ using a multivariate Gaussian $\mathcal{N}(f(z), \sigma^2 \cdot I)$. $f(z)$ will be modeled using a neural network. $\sigma$ is a hyperparameter that multiplies the identity matrix $I$.

Mar 31, 2025 · A Variational Autoencoder (VAE) is a deep learning model that generates new data by learning a probabilistic representation of input data. Unlike standard autoencoders, VAEs encode inputs into a latent space as probability distributions …

Mar 10, 2021 · Variational Autoencoders are generative models with an encoder-decoder architecture. Just like a standard autoencoder, VAEs are trained in an unsupervised manner where the reconstruction error between the input x and the reconstructed input x’ is minimized.

So, the method is called variational autoencoder (VAE). The encoder maps the data distribution, which is complex, to approximately an Gaussian distribution. The decoder maps a Gaussian distribution to the data distribution. Fake images generated by picking points in the latent space and map them back to the data space using the decoder. q (zjx(i)).