Variational Autoencoders
Why Data Science Stack Exchange recommends it
Data Science Stack Exchange users link it as an accessible walkthrough of VAEs when asked how they work, how they differ from GANs, or how to use them for tasks like anomaly detection and synthetic data.
Recommended by 1 people across 1 different months, 2018 to 2023.
What it is
A 15-minute technical explainer from the Arxiv Insights channel on variational autoencoders. It starts with plain autoencoders, then covers VAEs, disentangled beta-VAEs, the reparameterization trick and applications such as DARLA in reinforcement learning, with links to the original papers.
What people said
Recommends Shubham Panchal · 2019-10 · Data Science Stack Exchange · on “How do autoencoders reconstruct images/color?”
Autoencoders ( AE ) are obviously unsupervised learning algorithms as they try to reconstruct the input or something similar to their input. To get a better understanding, we may use autoencoder to colourizing grayscale images. Given an input grayscale image, the AE will predict coloured images. I have tried one such AE here [LINK] . Autoencoders learn to represent the given grayscale image into a latent ( compressed ) form. The encoder does this job. Now when the image has been brought down to its latent form, the decoder reconstructs the coloured image using that latent representation. Due to this ability of autoencoders, they are used in image colorization, neural implanting and even gene
Cites PyRsquared · 2018-05 · Data Science Stack Exchange · on “Which machine learning algorithm to choose?”
If you want to use an unsupervised method i.e if your data is not labelled with classes, then something like k-means clustering [LINK] may be your best bet to find patterns in the data. Alternatively, if you want to do anomaly detection, 2 possible options are Assuming your data is normally distributed, you could fit a Gaussian [LINK] to your data and calculate the Probability Density Function (PDF). Once you have the PDF, you can set a threshold probability, below which a data point could be classified as an anomaly If you have enough data, use a Variational Autoencoder [this video] neural network. Very roughly speaking, you train this on all the data you deem to be “normal” (the neural network
Cites J.D. · 2020-08 · Data Science Stack Exchange · on “What is an autoencoder?”
I am a student and I am studying machine learning. I am focusing on deep generative models , and in particular to autoencoders and variational autoencoders (VAE) . I am trying to understand the concept, but I am having some problems. So far, I have understood that an autoencoder takes an input, for example an image, and wants to reduce this image into a latent space , which should contain the underlying features of the dataset, with an operation of encoding , then, with an operation of decoding , it reconstrunct the image which has lost some information due to the encoding part. After this, with a loss function, it reconstruct the latent space and so get the latent features. [LINK] about the
Cites NevMthw · 2023-05 · Data Science Stack Exchange · on “How does variational autoencoders actually work in comparison to GAN?”
I want to know about how variational autoencoders work. I am currently working in a company and we want to incorporate variational autoencoders for creating synthetic data. I have questions regarding this method though, is this the only way to generate synthetic or artificial data? Is there a difference between VAE and GANs, is one preferred over the other? I am also not a person with a lot of mathematical background and a bit wary on the implementation of it. Finally, I have gone through many links and videos on the implementation through PyTorch and Tensorflow. Are both similar in implementation? I went through this link: [this video] However, still not fully grasped a simpler way to implement