Tutorial on Variational Autoencoders
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Summary
This tutorial introduces the intuitions behind VAEs, explains the mathematics behind them, and describes some empirical behavior.
- Type
- preprint
- Published
- 2016-06-19
- Cited by
- 2,031
- References
- 24
- Access
- Open access
- OpenAlex
- https://openalex.org/W2467604901
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10510670
Keywords
Computer science, Artificial intelligence, Artificial neural network, Segmentation, Function (biology)
References
- Deep Boltzmann Machines
- The Helmholtz Machine
- Deep Convolutional Inverse Graphics Network
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- Auto-Encoding Variational Bayes
- The "wake-sleep" algorithm for unsupervised neural networks.
- Extracting and composing robust features with denoising autoencoders
- Keeping the neural networks simple by minimizing the description length of the weights
- Sample-based non-uniform random variate generation
- Autoencoders, Minimum Description Length and Helmholtz Free Energy
- Semi-supervised Learning with Deep Generative Models
- Deep Generative Stochastic Networks Trainable by Backprop
- A Fast Learning Algorithm for Deep Belief Nets
- Emergence of simple-cell receptive field properties by learning a sparse code for natural images
- Caffe: Convolutional Architecture for Fast Feature Embedding
- ImageNet classification with deep convolutional neural networks
- Learning Structured Output Representation using Deep Conditional Generative Models
- An Uncertain Future: Forecasting from Static Images Using Variational Autoencoders
- DRAW: A Recurrent Neural Network For Image Generation
- Adam: A Method for Stochastic Optimization
Cited by
- Mechanism of glucocerebrosidase activation and dysfunction in Gaucher disease unraveled by molecular dynamics and deep learning
- Modeling Grasp Motor Imagery
- Authoring image decompositions with generative models
- A Point Set Generation Network for 3D Object Reconstruction from a Single Image
- StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks
- Bridging the Gap between Open Loop Tests and Statistical Validation for Highly Automated Driving
- Deep stochastic radar models
- On the Origin of Deep Learning
- Deep predictive policy training using reinforcement learning
- Deep recurrent music writer: Memory-enhanced variational autoencoder-based musical score composition and an objective measure
- TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network
- Lifelong Generative Modeling
- Towards meaningful physics from generative models
- Generative-Discriminative Variational Model for Visual Recognition
- Inferring single-trial neural population dynamics using sequential auto-encoders
- The shape variational autoencoder: A deep generative model of part‐segmented 3D objects
- Unsupervised feature learning for road segmentation with deep neural networks using generative models and auxiliary tasks
- Variational Inference via Transformations on Distributions
- The difference learning of hidden layer between autoencoder and variational autoencoder
- Learning to Reconstruct 3D Structures for Occupancy Mapping
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