Variational Lossy Autoencoder
This paper introduces the Variational Lossy Autoencoder (VLAE), a model that combines Variational Autoencoders (VAEs) with autoregressive models to learn global representations of data while discarding irrelevant details like texture. VLAE achieves state-of-the-art results on various density estimation tasks.
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Chapters
01Abstract
A Variational Autoencoder model combines VAEs with autoregressive models to learn global representations by encoding data in a lossy fashion.
1:32Explained02Introduction
Representation learning aims to disentangle causal factors of data, but generative modeling is ill-posed without further assumptions, leading to the proposal of hybrid models combining VAEs and autoregressive models.
2:03Explained03VAES DO NOT AUTOENCODE IN GENERAL
Variational Autoencoders (VAEs) do not always autoencode, and the latent code is often ignored unless the decoder is weakened, a phenomenon understandable through Bits-Back Coding.
1:40Explained04Information Preference
The information preference property of VAEs causes latent codes to be ignored when powerful decoders can model data without them, leading to inefficiency in Bits-Back Coding.
2:32Explained05Variational Lossy Autoencoder
The Variational Lossy Autoencoder (VLAE) exploits information preference by using autoregressive models with constrained receptive fields to control what information is captured in the latent representation.
1:31Explained06Learned Prior with Autoregressive Flow
An Autoregressive Flow (AF) prior in VLAE is equivalent to an Inverse Autoregressive Flow (IAF) posterior, improving Bits-Back Coding efficiency and offering a more expressive generative model at no additional cost.
1:41Explained07Experiments
VLAE is evaluated on 2D image datasets, demonstrating its ability to learn lossy codes for global statistics and achieving state-of-the-art results in density estimation.
1:54Explained08Density Estimation
VLAE, utilizing AF priors and autoregressive decoders, outperforms VAEs with IAF priors and achieves new state-of-the-art results on various binary image datasets.
1:52Explained09Natural Images: CIFAR10
VLAE is applied to CIFAR10, achieving state-of-the-art performance among variational latent-variable models and outperforming most other tractable likelihood models.
1:45Explained10Lossy Compression on CIFAR10
VLAE's receptive field size influences the information captured in lossy codes, with larger fields retaining rougher shapes and grayscale receptive fields preserving color information.
1:37Explained11Related Work
This work combines variational autoencoders with continuous latent variables and neural autoregressive models, applying a novel architecture for autoregression.
1:37Explained12Related Work
This work combines variational autoencoders with continuous latent variables and neural autoregressive models, applying a novel architecture for autoregression.
1:57Explained13Related Work
This text reviews prior work on autoregressive models, latent variable models, and VAEs, highlighting connections and differences with the current research.
2:04Explained14Conclusion
This paper proposes a VAE model that functions as a lossy compressor, offering controllable representations and improved density estimation at the cost of slower generation.
1:21Explained15References
This section lists numerous academic papers and publications relevant to the research, covering topics in machine learning, generative models, and neural networks.
1:31Explained16References
This section lists further academic papers and publications relevant to the research, focusing on variational autoencoders, generative models, and neural network architectures.
1:31Explained17Appendix A: Detailed Experiment Setup for Binary Images
This section details the experimental setup for binary images, including the ResNet VAE architecture, PixelCNN configuration, latent code dimension, and training parameters.
1:31Explained18Appendix B: Additional Experiment Setup for CIFAR10
This section outlines the experimental setup for CIFAR10, detailing latent code representation, prior distribution, encoder/decoder architectures, and the PixelCNN++ decoder used.
1:36Explained19Appendix C: Soft Free Bits
This section introduces a modified "free bits" technique for optimizing VAEs, proposing a smoother objective function that encourages sufficient latent code usage while allowing gradual KL divergence increase.
20Appendix D: Autoregressive Decoder Without Autoregressive Prior
This section investigates the performance of an autoregressive decoder with and without an autoregressive prior, demonstrating that incorporating an autoregressive latent code improves density estimation and information transmission.
21Figure 4: CIFAR10: Generated samples for different models
This figure displays generated samples from CIFAR10 for various models, illustrating the visual quality of generated images at different bit rates.