Self-Reflective Variational Autoencoder
- URL: http://arxiv.org/abs/2007.05166v1
- Date: Fri, 10 Jul 2020 05:05:26 GMT
- Title: Self-Reflective Variational Autoencoder
- Authors: Ifigeneia Apostolopoulou, Elan Rosenfeld, Artur Dubrawski
- Abstract summary: Variational Autoencoder (VAE) is a powerful framework for learning latent variable generative models.
We introduce a solution, which we call self-reflective inference.
We empirically demonstrate the clear advantages of matching the variational posterior to the exact posterior.
- Score: 21.054722609128525
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Variational Autoencoder (VAE) is a powerful framework for learning
probabilistic latent variable generative models. However, typical assumptions
on the approximate posterior distribution of the encoder and/or the prior,
seriously restrict its capacity for inference and generative modeling.
Variational inference based on neural autoregressive models respects the
conditional dependencies of the exact posterior, but this flexibility comes at
a cost: such models are expensive to train in high-dimensional regimes and can
be slow to produce samples. In this work, we introduce an orthogonal solution,
which we call self-reflective inference. By redesigning the hierarchical
structure of existing VAE architectures, self-reflection ensures that the
stochastic flow preserves the factorization of the exact posterior,
sequentially updating the latent codes in a recurrent manner consistent with
the generative model. We empirically demonstrate the clear advantages of
matching the variational posterior to the exact posterior - on binarized MNIST,
self-reflective inference achieves state-of-the art performance without
resorting to complex, computationally expensive components such as
autoregressive layers. Moreover, we design a variational normalizing flow that
employs the proposed architecture, yielding predictive benefits compared to its
purely generative counterpart. Our proposed modification is quite general and
complements the existing literature; self-reflective inference can naturally
leverage advances in distribution estimation and generative modeling to improve
the capacity of each layer in the hierarchy.
Related papers
- Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures [14.551812310439004]
We introduce an untrained forward model residual block within the model-based architecture to match the data consistency in the measurement domain for each instance.
Our approach offers a unified solution that is less parameter-sensitive, requires no additional data, and enables simultaneous fitting of the forward model and reconstruction in a single pass.
arXiv Detail & Related papers (2024-03-07T19:02:13Z) - Disentanglement via Latent Quantization [60.37109712033694]
In this work, we construct an inductive bias towards encoding to and decoding from an organized latent space.
We demonstrate the broad applicability of this approach by adding it to both basic data-re (vanilla autoencoder) and latent-reconstructing (InfoGAN) generative models.
arXiv Detail & Related papers (2023-05-28T06:30:29Z) - Exploiting Diffusion Prior for Real-World Image Super-Resolution [75.5898357277047]
We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution.
By employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model.
arXiv Detail & Related papers (2023-05-11T17:55:25Z) - Enhancing Deep Traffic Forecasting Models with Dynamic Regression [15.31488551912888]
This paper introduces a dynamic regression (DR) framework to enhance existing traffic forecasting models by structured learning for the residual process.
We evaluate the effectiveness of the proposed framework on deep traffic forecasting models using both speed and flow datasets.
arXiv Detail & Related papers (2023-01-17T01:12:44Z) - Variational Laplace Autoencoders [53.08170674326728]
Variational autoencoders employ an amortized inference model to approximate the posterior of latent variables.
We present a novel approach that addresses the limited posterior expressiveness of fully-factorized Gaussian assumption.
We also present a general framework named Variational Laplace Autoencoders (VLAEs) for training deep generative models.
arXiv Detail & Related papers (2022-11-30T18:59:27Z) - Adversarial and Contrastive Variational Autoencoder for Sequential
Recommendation [25.37244686572865]
We propose a novel method called Adversarial and Contrastive Variational Autoencoder (ACVAE) for sequential recommendation.
We first introduce the adversarial training for sequence generation under the Adversarial Variational Bayes framework, which enables our model to generate high-quality latent variables.
Besides, when encoding the sequence, we apply a recurrent and convolutional structure to capture global and local relationships in the sequence.
arXiv Detail & Related papers (2021-03-19T09:01:14Z) - Anomaly Detection of Time Series with Smoothness-Inducing Sequential
Variational Auto-Encoder [59.69303945834122]
We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series.
Our model parameterizes mean and variance for each time-stamp with flexible neural networks.
We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
arXiv Detail & Related papers (2021-02-02T06:15:15Z) - Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [55.28436972267793]
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors.
We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method.
Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
arXiv Detail & Related papers (2020-10-25T18:51:15Z) - Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders [22.54887526392739]
We propose a novel approach to training models with deep-latent hierarchies based on Optimal Transport.
We show that our method enables the generative model to fully leverage its deep-latent hierarchy, avoiding the well known "latent variable collapse" issue of VAEs.
arXiv Detail & Related papers (2020-10-07T15:04:20Z) - Normalizing Flows with Multi-Scale Autoregressive Priors [131.895570212956]
We introduce channel-wise dependencies in their latent space through multi-scale autoregressive priors (mAR)
Our mAR prior for models with split coupling flow layers (mAR-SCF) can better capture dependencies in complex multimodal data.
We show that mAR-SCF allows for improved image generation quality, with gains in FID and Inception scores compared to state-of-the-art flow-based models.
arXiv Detail & Related papers (2020-04-08T09:07:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.