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Found 7 papers, 1 papers with code
HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models
We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model.
Stochastic Variational Optimization
These approaches are of particular interest because they are parallelizable.
Variational f-divergence Minimization
Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution.
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks
In this work we show, for the first time, that we can successfully train generative models which utilize binary neural networks.
3D Scene Compression through Entropy Penalized Neural Representation Functions
We unify these steps by directly compressing an implicit representation of the scene, a function that maps spatial coordinates to a radiance vector field, which can then be queried to render arbitrary viewpoints.
Training generative latent models by variational f-divergence minimization
Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific form of f-divergence between the model and data distribution.
A Memory Transformer Network for Incremental Learning
We study class-incremental learning, a training setup in which new classes of data are observed over time for the model to learn from.
