Variational autoencoders learn deep latent models.
problem Learning deep latent-variable models.
method Principled framework using variational inference.
result Introduction to variational autoencoders and extensions.
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48 results for “Variational AutoEncoders”
Diffusion Variational Autoencoders capture topological properties of datasets.
problem Standard VAEs struggle with topological properties of certain datasets.
method Introduces Diffusion VAEs with transition kernels of Brownian motion on arbitrary manifolds.
result Diffusion VAEs can capture topological properties of synthetic datasets.
Paper proposes a new autoencoder to handle complex data structures.
problem Challenges in latent variable learning with complex data.
method Variational Composite Autoencoder using hierarchical latent variable model.
result Experimental results show the new model's advantages.
This work prevents variational autoencoders from collapsing by adding an auxiliary decoder.
problem Variational autoencoders can collapse into autodecoders, losing semantic information.
method Adding an auxiliary decoder to regularize the latent space.
result Auxiliary decoders increase semantic information in the latent space and reconstructions.
This tutorial derives the VAE loss function under Gaussian assumptions.
problem Computational intractability of posterior distributions in Bayesian machine learning.
method Derives the variational lower bound loss function of a standard VAE.
result The Kullback-Leibler divergence has a closed form solution under Gaussian assumptions.
This paper improves VAEs with regularization for better image reconstruction.
problem Improving image reconstruction quality in Variational Autoencoders.
method Least square loss function with regularization for better approximation of data.
result Least square loss function leads to better reconstructed images and faster training.
We extend Stochastic Gradient Variational Bayes to perform posterior inference for the weights of Stick-Breaking processes. This development allows us to define a Stick-Breaking Variational Autoencoder (SB-VAE), a Bayesian nonparametric version of the variational autoencoder that has a latent representation with stocha…
This paper quantifies how varying affects disentanglement in variational autoencoders.
problem The challenge is to quantify the effects of on disentanglement in variational autoencoders.
method The approach involves training multiple variational autoencoders with the same value and analyzing the disentanglement measures.
result There is consistent variance in one disentanglement measure, indicating negative effects on discriminative ability.
Dr.VAE improves drug response prediction accuracy.
problem Improving accuracy of drug response prediction.
method Two deep generative models based on Variational Autoencoders.
result Dr.VAE outperforms benchmarks by 3-11% AUROC and 2-30% AUPR.
A new speech enhancement method using variational autoencoders.
problem Improving speech quality in noisy environments.
method Using a variational autoencoder as a speech model, trained with unsupervised noise modeling.
result The method outperforms existing techniques in speech enhancement.
Variational autoencoders model water Cherenkov detector data.
problem Modeling generative distribution of water Cherenkov detector data.
method Variational autoencoders and normalizing flows.
result Demonstrated capability of variational autoencoders for generative modelling.
Variational autoencoders often collapse, showing latent variables are non-identifiable.
problem Posterior collapse in variational autoencoders due to non-identifiable latent variables.
method Proves latent variable non-identifiability causes posterior collapse. Proposes latent-identifiable models using Brenier maps and input convex neural networks.
result Latent-identifiable models resolve posterior collapse and provide meaningful representations.
The paper interprets VQ-VAE loss as a form of information bottleneck.
problem Understanding the VQ-VAE loss function.
method Interpreted VQ-VAE loss as variational deterministic information bottleneck (VDIB) and variational information bottleneck (VIB).
result VQ-VAE loss can be derived from VDIB and approximated by VIB.
DAEs can generate images without additional loss terms, inheriting VAE properties.
problem Difficulty in using VAEs for practical generative modelling.
method Empirical exploration of DAEs for image generation without novel methods.
result DAEs can generate images successfully without additional loss terms.
InfoVAE improves variational autoencoders by better training inference distributions.
problem Inaccurate amortized inference and latent variable neglect in variational autoencoders.
method Proposes new training objectives (InfoVAE) to improve inference quality and latent feature use.
result Significantly improves variational posterior quality and effective latent feature use.
Study reveals the regularization effect of variational distributions in VAEs.
problem Understanding the regularization role of variational distributions in VAEs.
method Analyzed the role of variational family in VAEs and studied the regularization effect on local geometry.
result Uncovered the implicit regularizer in the -VAE objective and proposed a deterministic autoencoding objective.
Derives MVAE objective from marginal joint log-likelihood.
problem Training Multi-Modal Variational Autoencoders (MVAEs).
method Derives trainable evidence lower bound from marginal joint log-likelihood.
result Derives MVAE objective from marginal joint log-likelihood.
Combines variational autoencoders with normalizing flows for faster training.
problem Training normalizing flow models like Glow is slow and requires deep architectures.
method Integrates Glow with a variational autoencoder to speed up training.
result The combined model achieves similar image quality and likelihood to Glow but trains faster.
Variational autoencoders struggle with inference quality due to recognition network limitations.
problem Inference suboptimality in variational autoencoders.
method Examined approximate inference in terms of variational distribution capacity and recognition network quality.
result Inference quality is more influenced by recognition network limitations than variational distribution complexity.
New method uses adversarial networks to improve image quality in autoencoders.
problem Blurriness in autoencoder-generated images due to Gaussian assumptions.
method Integrates adversarial networks to optimize parameters without Gaussian assumptions.
result Improves image quality by allowing better representation of multimodal distributions.
Wasserstein variational inference uses optimal transport for stable likelihood-free training.
problem Approximate Bayesian inference with stability and flexibility for implicit distributions.
method Optimal transport theory, Sinkhorn iterations, and backpropagation.
result Stable likelihood-free training method for autoencoders and probabilistic programs.
Paper proposes efficient multivariate spatial Fay-Herriot models using variational autoencoders.
problem Estimating population characteristics in small areas with limited data.
method Integrates multivariate spatial Fay-Herriot model with variational autoencoders to leverage spatial structure efficiently.
result Significant computational efficiency improvements for high-dimensional datasets.
A neural model samples arbitrary features conditioned on some inputs.
problem Sampling arbitrary features conditioned on some inputs.
method Variational autoencoder with stochastic variational Bayes training.
result Effectiveness and diversity of generated samples on synthetic data and feature problems.
VAE improves MSI data analysis for tissue sub-types.
problem Analyzing MSI data from unprocessed samples.
method Applied Variational Autoencoders for data reduction and pattern detection.
result VAEs outperform standard methods in detecting tissue sub-types.
Novel variational autoencoder for generative and classification tasks.
problem Developing a robust generative model for various tasks.
method A novel variational autoencoder with specific latent variables and ordinality enforcement.
result Comparable performance in generative and classification tasks compared to baselines.
Develops VAEs with graphical models for interpretable representations.
problem Creating interpretable representations in complex, high-dimensional data.
method Incorporates structured graphical models into VAE encoders for approximate variational inference.
result Induces interpretable representations with deep generative models under structural constraints.
Reinterprets IWAE as optimizing a tighter variational bound.
problem Improving variational lower bounds in autoencoders.
method Derives and visualizes a tighter lower bound using a more complex distribution.
result IWAE optimizes the standard variational lower bound with a complex distribution.
Proposes variational autoencoder for efficient MMSE estimation.
problem Efficient parameterized MMSE estimation for noisy observations.
method Variational autoencoder models data distribution, approximates MMSE.
result Proposed estimator performs well compared to state-of-the-art.
Batch norm with entropic regularization turns deterministic autoencoders into generative modelscs.LG
Batch normalization with regularization turns deterministic autoencoders into generative models.
problem Creating generative models from deterministic autoencoders.
method Using batch normalization as a source of non-determinism and adding entropic regularization.
result Deterministic autoencoders can be transformed into generative models with similar performance to variational autoencoders.
InfoMax Autoencoder maximizes mutual information for robust data representation.
problem Learning robust data representations from raw data.
method Explicitly maximizes mutual information between input and hidden representation.
result IMAE outperforms state-of-the-art models in clustering performance.
A new model for multiview data analysis using graph autoencoders.
problem Nonlinear multiview canonical correlation analysis for large datasets.
method Variational approach with graph convolutional neural networks.
result Competitive performance on classification, clustering, and recommendation tasks.
PRI-VAE learns disentangled representations by optimizing principle-of-relevant-information.
problem Learning disentangled representations under VAE framework remains unknown.
method Proposes PRI-VAE, a novel learning objective to optimize disentanglement.
result Demonstrates effectiveness of PRI-VAE on four benchmark datasets.
Score matching fails to train VAEs robustly, revealing autoencoding loss insights.
problem Catastrophic failure of variational score matching on VAE models.
method Analysis of existing variational score matching objectives and their equivalence to autoencoding losses.
result Score matching methods fail to produce robust VAE models, predicting poor performance.
Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult to train which limits the improvements obtained using these highly expressive models. We propose a new inference model, the Ladder Variational Autoencoder, that…
New method learns disentangled discrete representations using categorical variational autoencoders.
problem Learning disentangled representations from discrete latent spaces.
method Replaced standard Gaussian VAE with a categorical VAE to mitigate rotational invariance.
result Categorical distributions improve learning of disentangled representations.
Speech enhancement improved using variational autoencoders and alpha-stable noise models.
problem Improving speech quality in noisy environments.
method Deep generative model using variational autoencoders with alpha-stable noise model.
result The proposed method outperforms conventional approaches in speech intelligibility and quality.
Lower bound for VAE training objective for binary data.
problem Finding a lower bound for the ELBO of Bernoulli VAE.
method Interpretable lower bound, modified initialization, faster training architecture, PCA for latent space dimension.
result Theoretical result and improved performance of new architecture.
VANO uses neural operators for unsupervised learning of functional data.
problem Learning operators between infinite dimensional spaces for functional data.
method Variational Autoencoding Neural Operators (VANO) approach.
result VANO can learn and reconstruct functional data without supervision.
A scalable factorized Gaussian process VAE for faster inference.
problem Inference bottlenecks in Gaussian process VAEs.
method Factorizes latent kernel across auxiliary features, leveraging independence.
result Significant speed-up in inference time (in theory and practice).
EVGAE improves VGAE's latent representation learning by mitigating over-pruning.
problem Over-pruning in VGAE limits latent variable capacity and diversity.
method EVGAE uses epitomic approach with multiple sparse VGAE models (epitomes) to increase active latent units and improve generative ability.
result EVGAE outperforms VGAE in generative ability and link prediction on citation networks.
Optimizes variational autoencoder for detecting missing data in Mars rover transmissions.
problem Detecting missing data in Mars rover transmissions to prevent volume loss and corruption.
method Applies derivative-free optimization to tune variational autoencoder.
result Improves variational autoencoder's ability to detect missing data, aiding GDSA team.
Automates molecule design with a novel variational autoencoder.
problem Designing molecules based on specific chemical properties.
method Junction tree variational autoencoder generating tree-structured scaffolds and combining them into molecules.
result Significantly outperforms previous models on molecular generation and optimization tasks.
Improves VAE training by refining variational parameters with BSVI.
problem Amortized inference in VAEs leads to suboptimal variational parameters and the amortization gap.
method Proposes BSVI, a refinement procedure using SVI's importance weights.
result Training VAEs with BSVI yields improved performance compared to SVI.
A new model uses past consumption history to predict user preferences.
problem Predicting user preferences from past consumption history.
method Sequential Variational Autoencoder with a recurrent neural network.
result The model outperforms state-of-the-art methods by significant margins.
A method trains discrete autoencoders efficiently.
problem Training models with discrete latent variables is difficult.
method Variational autoencoder framework with backpropagation through discrete variables.
result Efficiently learns class and realization of objects from unsupervised data.
ED-VAE improves VAEs by explicitly including entropy components in ELBO.
problem Limitations of traditional VAEs with ELBO in generating high-quality samples and interpreting latent spaces.
method Introduces ED-VAE, a re-formulation of ELBO that includes entropy and cross-entropy components.
result Significantly enhances model flexibility and improves interpretability and generative performance.
Develops VAEs for learning complex physical systems from data.
problem Learning low-dimensional representations of nonlinear physical systems.
method Variational Autoencoders with manifold latent spaces.
result Effective in learning nonlinear Burgers equation and constrained mechanical systems.
A new autoencoder learns expressive posterior and conditional likelihood distributions.
problem Learning more expressive posterior and conditional likelihood distributions.
method Implicit autoencoder using two generative adversarial networks for reconstruction and regularization.
result Implicit autoencoder can disentangle content and style information.