Variational Autoencoders Without the Variation (original) (raw)
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Adversarial Variational Autoencoders to extend and improve generative model
Preprints, 2023
Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, VAE and GAN is unified into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.
Variations in Variational Autoencoders - A Comparative Evaluation
IEEE Access
Variational Auto-Encoders (VAEs) are deep latent space generative models which have been immensely successful in many applications such as image generation, image captioning, protein design, mutation prediction, and language models among others. The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data can be generated from the encoded distribution. This concept has led to tremendous research and variations in the design of VAEs in the last few years creating a field of its own, referred to as unsupervised representation learning. This paper provides a muchneeded comprehensive evaluation of the variations of the VAEs based on their end goals and resulting architectures. It further provides intuition as well as mathematical formulation and quantitative results of each popular variation, presents a concise comparison of these variations, and concludes with challenges and future opportunities for research in VAEs.
A Comparative Study on Variational Autoencoders and Generative Adversarial Networks
IEEE, 2019
Generative Adversarial Networks (GAN) have been remarkable at generating artificial data, especially realistic looking images. This learning framework has proven itself to be effective in synthetic image generation, semantic image hole filling, semantic image editing, style transfer and many more. On the other hand, variational auto-encoders (VAE) have also been quite effective, so much so that mathematically it is often more accurate at generating images resembling to its original dataset. Nevertheless, images generated by VAE suffer from blurriness and are generally less realistic looking from human perception. In this paper we take a broad view on both systems and propose a theoretical approach to combine them and bring out the best of both.
AVAE: Adversarial Variational Auto Encoder
2020 25th International Conference on Pattern Recognition (ICPR), 2021
Among the wide variety of image generative models, two models stand out: Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs can produce realistic images, but they suffer from mode collapse and do not provide simple ways to get the latent representation of an image. On the other hand, VAEs do not have these problems, but they often generate images less realistic than GANs. In this article, we explain that this lack of realism is partially due to a common underestimation of the natural image manifold dimensionality. To solve this issue we introduce a new framework that combines VAE and GAN in a novel and complementary way to produce an auto-encoding model that keeps VAEs properties while generating images of GAN-quality. We evaluate our approach both qualitatively and quantitatively on five image datasets.
Cornell University - arXiv, 2022
We propose a multi-layer variational autoencoder method, we call HR-VQVAE, that learns hierarchical discrete representations of the data. By utilizing a novel objective function, each layer in HR-VQVAE learns a discrete representation of the residual from previous layers through a vector quantized encoder. Furthermore, the representations at each layer are hierarchically linked to those at previous layers. We evaluate our method on the tasks of image reconstruction and generation. Experimental results demonstrate that the discrete representations learned by HR-VQVAE enable the decoder to reconstruct high-quality images with less distortion than the baseline methods, namely VQVAE and VQVAE-2. HR-VQVAE can also generate high-quality and diverse images that outperform state-of-the-art generative models, providing further verification of the efficiency of the learned representations. The hierarchical nature of HR-VQVAE i) reduces the decoding search time, making the method particularly suitable for high-load tasks and ii) allows to increase the codebook size without incurring the codebook collapse problem.
Balancing Reconstruction Error and Kullback-Leibler Divergence in Variational Autoencoders
IEEE Access, 2020
Likelihood-based generative frameworks are receiving increasing attention in the deep learning community, mostly on account of their strong probabilistic foundation. Among them, Variational Autoencoders (VAEs) are reputed for their fast and tractable sampling and relatively stable training, but if not properly tuned they may easily produce poor generative performances. The loss function of Variational Autoencoders is the sum of two components, with somehow contrasting effects: the reconstruction loss, improving the quality of the resulting images, and the Kullback-Leibler divergence, acting as a regularizer of the latent space. Correctly balancing these two components is a delicate issue, and one of the major problems of VAEs. Recent techniques address the problem by allowing the network to learn the balancing factor during training, according to a suitable loss function. In this article, we show that learning can be replaced by a simple deterministic computation, expressing the balancing factor in terms of a running average of the reconstruction error over the last minibatches. As a result, we keep a constant balance between the two components along training: as reconstruction improves, we proportionally decrease KL-divergence in order to prevent its prevalence, that would forbid further improvements of the quality of reconstructions. Our technique is simple and effective: it clarifies the learning objective for the balancing factor, and it produces faster and more accurate behaviours. On typical datasets such as Cifar10 and CelebA, our technique sensibly outperforms all previous VAE architectures with comparable parameter capacity.
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples. Research has fragmented into various interconnected approaches, each of which make trade-offs including run-time, diversity, and architectural restrictions. In particular, this compendium covers energy-based models, variational autoencoders, generative adversarial networks, autoregressive models, normalizing flows, in addition to numerous hybrid approaches. These techniques are compared and contrasted, explaining the premises behind each and how they are interrelated, while reviewing current state-of-the-art advances and implementations.
Inverting Variational Autoencoders for Improved Generative Accuracy
2016
Recent advances in semi-supervised learning with deep generative models have shown promise in generalizing from small labeled datasets ($\mathbf{x},\mathbf{y}$) to large unlabeled ones ($\mathbf{x}$). In the case where the codomain has known structure, a large unfeatured dataset ($\mathbf{y}$) is potentially available. We develop a parameter-efficient, deep semi-supervised generative model for the purpose of exploiting this untapped data source. Empirical results show improved performance in disentangling latent variable semantics as well as improved discriminative prediction on Martian spectroscopic and handwritten digit domains.
NCP-VAE: Variational Autoencoders with Noise Contrastive Priors
ArXiv, 2020
Variational autoencoders (VAEs) are one of the powerful likelihood-based generative models with applications in various domains. However, they struggle to generate high-quality images, especially when samples are obtained from the prior without any tempering. One explanation for VAEs' poor generative quality is the prior hole problem: the prior distribution fails to match the aggregate approximate posterior. Due to this mismatch, there exist areas in the latent space with high density under the prior that do not correspond to any encoded image. Samples from those areas are decoded to corrupted images. To tackle this issue, we propose an energy-based prior defined by the product of a base prior distribution and a reweighting factor, designed to bring the base closer to the aggregate posterior. We train the reweighting factor by noise contrastive estimation, and we generalize it to hierarchical VAEs with many latent variable groups. Our experiments confirm that the proposed noise ...
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents
Cornell University - arXiv, 2022
Diffusion Probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable latent space, and are slow at generation. On the other hand, Variational Autoencoders (VAEs) typically have access to a low-dimensional latent space but exhibit poor sample quality. Despite recent advances, VAEs usually require high-dimensional hierarchies of the latent codes to generate high-quality samples. We present DiffuseVAE, a novel generative framework that integrates VAE within a diffusion model framework, and leverage this to design a novel conditional parameterization for diffusion models. We show that the resulting model can improve upon the unconditional diffusion model in terms of sampling efficiency while also equipping diffusion models with the low-dimensional VAE inferred latent code. Furthermore, we show that the proposed model can generate high-resolution samples and exhibits synthesis quality comparable to state-of-the-art models on standard benchmarks. Lastly, we show that the proposed method can be used for controllable image synthesis and also exhibits out-of-the-box capabilities for downstream tasks like image super-resolution and denoising. For reproducibility, our source code is publicly available at https://github.com/kpandey008/DiffuseVAE. 1