Variational autoencoders (VAE) employ Bayesian inference to interpret sensory inputs, mirroring processes that occur in primate vision across both ventral (Higgins et al., 2021) and dorsal (Vafaii et al., 2023) pathways.

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.

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.

Aug 13, 2024 · A Variational Autoencoder (VAE) extends this by encoding inputs into a probability distribution, typically Gaussian, over the latent space. This probabilistic approach allows VAEs to sample from the latent distribution, enabling the generation of new, diverse data instances and better modeling of data variability.

Variational Autoencoders (VAEs) address some of the limitations of traditional autoencoders by introducing a probabilistic approach to encoding and decoding. The motivation behind VAEs lies in their ability to generate new data samples by sampling from a learned distribution in the latent space rather than from a latent vector as was the case ...

Apr 15, 2019 · The ability of a variational autoencoder to generate realistic images by random sampling is impressive. Today, new variants of variational autoencoders exist for other data generation applications.

Nov 25, 2020 · Autoencoders allow you to replicate the works of Picasso, scale down terabytes of data, and denoise grainy images from security cameras. Let’s first start with how to make general autoencoders,...

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.

Oct 4, 2024 · Variational Autoencoders (VAEs) are a type of generative model used in machine learning and statistics to generate new data samples similar to those in a given dataset. They are particularly...