DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors
Boltzmann machines are powerful distributions that have been shown to be an effective prior over binary latent variables in variational autoencoders (VAEs). However, previous methods for training discrete VAEs have used the evidence lower bound and not the tighter importance-weighted bound. We propose two approaches for relaxing Boltzmann machines to continuous distributions that permit training with importance-weighted bounds. These relaxations are based on generalized overlapping transformations and the Gaussian integral trick. Experiments on the MNIST and OMNIGLOT datasets show that these relaxations outperform previous discrete VAEs with Boltzmann priors. An implementation which reproduces these results is available at https://github.com/QuadrantAI/dvae .
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
Training of discrete latent variable models remains challenging because passing gradient information through discrete units is difficult. We propose a new class of smoothing transformations based on a mixture of two over…
Image GenerationAnomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Model
Deep generative learning cannot only be used for generating new data with statistical characteristics derived from input data but also for anomaly detection, by separating nominal and anomalous instances based on their r…
Anomaly DetectionTime SeriesEdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders
Codebook collapse is a common problem in training deep generative models with discrete representation spaces like Vector Quantized Variational Autoencoders (VQ-VAEs). We observe that the same problem arises for the alter…
Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation
In this paper, we propose a latent-variable generative model called mixture of dynamical variational autoencoders (MixDVAE) to model the dynamics of a system composed of multiple moving sources. A DVAE model is pre-train…
Audio Source SeparationMulti-Object TrackingObject TrackingTrajectory ModelingCaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulation
Calorimeter simulation is the most computationally expensive part of Monte Carlo generation of samples necessary for analysis of experimental data at the Large Hadron Collider (LHC). The High-Luminosity upgrade of the LH…