Incorporate Maximum Mean Discrepancy in Recurrent Latent Space for Sequential Generative Model

2021 ◽  
Author(s):  
Yuchi Zhang ◽  
Yongliang Wang ◽  
Yang Dong
Keyword(s):  
Generative Model ◽  
Maximum Mean Discrepancy ◽  
Latent Space

Supervised Determined Source Separation with Multichannel Variational Autoencoder

2019 ◽  
Vol 31 (9) ◽  
pp. 1891-1914 ◽  
Author(s):  
Hirokazu Kameoka ◽  
Li Li ◽  
Shota Inoue ◽  
Shoji Makino
Keyword(s):  
Source Separation ◽  
Generative Model ◽  
Separation Technique ◽  
Separation Performance ◽  
Unknown Parameters ◽  
Baseline Method ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Training Examples ◽  
Class Labels

This letter proposes a multichannel source separation technique, the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source-class labels, we can use the trained decoder distribution as a universal generative model capable of generating spectrograms conditioned on a specified class index. By treating the latent space variables and the class index as the unknown parameters of this generative model, we can develop a convergence-guaranteed algorithm for supervised determined source separation that consists of iteratively estimating the power spectrograms of the underlying sources, as well as the separation matrices. In experimental evaluations, our MVAE produced better separation performance than a baseline method.

Knowledge Preserving and Distribution Alignment for Heterogeneous Domain Adaptation

2022 ◽  
Vol 40 (1) ◽  
pp. 1-29
Author(s):  
Hanrui Wu ◽  
Qingyao Wu ◽  
Michael K. Ng
Keyword(s):  
Domain Adaptation ◽  
Iteration Scheme ◽  
Target Space ◽  
Target Domain ◽  
Maximum Mean Discrepancy ◽  
Learning Tasks ◽  
Feature Spaces ◽  
Latent Space ◽  
Laplacian Graph ◽  
Novel Model

Domain adaptation aims at improving the performance of learning tasks in a target domain by leveraging the knowledge extracted from a source domain. To this end, one can perform knowledge transfer between these two domains. However, this problem becomes extremely challenging when the data of these two domains are characterized by different types of features, i.e., the feature spaces of the source and target domains are different, which is referred to as heterogeneous domain adaptation (HDA). To solve this problem, we propose a novel model called Knowledge Preserving and Distribution Alignment (KPDA), which learns an augmented target space by jointly minimizing information loss and maximizing domain distribution alignment. Specifically, we seek to discover a latent space, where the knowledge is preserved by exploiting the Laplacian graph terms and reconstruction regularizations. Moreover, we adopt the Maximum Mean Discrepancy to align the distributions of the source and target domains in the latent space. Mathematically, KPDA is formulated as a minimization problem with orthogonal constraints, which involves two projection variables. Then, we develop an algorithm based on the Gauss–Seidel iteration scheme and split the problem into two subproblems, which are solved by searching algorithms based on the Barzilai–Borwein (BB) stepsize. Promising results demonstrate the effectiveness of the proposed method.

scholarly journals BEGAN v3: Avoiding Mode Collapse in GANs Using Variational Inference

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 688
Author(s):  
Sung-Wook Park ◽  
Jun-Ho Huh ◽  
Jong-Chan Kim
Keyword(s):  
Image Synthesis ◽  
Generative Model ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Compression Performance ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Latent Space ◽  
Boundary Equilibrium ◽  
Space Compression

In the field of deep learning, the generative model did not attract much attention until GANs (generative adversarial networks) appeared. In 2014, Google’s Ian Goodfellow proposed a generative model called GANs. GANs use different structures and objective functions from the existing generative model. For example, GANs use two neural networks: a generator that creates a realistic image, and a discriminator that distinguishes whether the input is real or synthetic. If there are no problems in the training process, GANs can generate images that are difficult even for experts to distinguish in terms of authenticity. Currently, GANs are the most researched subject in the field of computer vision, which deals with the technology of image style translation, synthesis, and generation, and various models have been unveiled. The issues raised are also improving one by one. In image synthesis, BEGAN (Boundary Equilibrium Generative Adversarial Network), which outperforms the previously announced GANs, learns the latent space of the image, while balancing the generator and discriminator. Nonetheless, BEGAN also has a mode collapse wherein the generator generates only a few images or a single one. Although BEGAN-CS (Boundary Equilibrium Generative Adversarial Network with Constrained Space), which was improved in terms of loss function, was introduced, it did not solve the mode collapse. The discriminator structure of BEGAN-CS is AE (AutoEncoder), which cannot create a particularly useful or structured latent space. Compression performance is not good either. In this paper, this characteristic of AE is considered to be related to the occurrence of mode collapse. Thus, we used VAE (Variational AutoEncoder), which added statistical techniques to AE. As a result of the experiment, the proposed model did not cause mode collapse but converged to a better state than BEGAN-CS.

scholarly journals MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space

2021 ◽  
Author(s):  
Sophie C Laturnus ◽  
Philipp Berens
Keyword(s):  
Ganglion Cells ◽  
Feature Space ◽  
Generative Models ◽  
Generative Model ◽  
Past Century ◽  
Retinal Ganglion ◽  
Cell Type ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Degree Of Similarity

For the past century, the anatomy of a neuron has been considered one of its defining features: The shape of a neuron`s dendrites and axon fundamentally determines what other neurons it can connect to. These neurites have been described using mathematical tools e.g. in the context of cell type classification, but generative models of these structures have only rarely been proposed and are often computationally inefficient. Here we propose MORPHVAE, a sequence-to-sequence variational autoencoder with spherical latent space as a generative model for neural morphologies. The model operates on walks within the tree structure of a neuron and can incorporate expert annotations on a subset of the data using semi-supervised learning. We develop our model on artificially generated toy data and evaluate its performance on dendrites of excitatory cells and axons of inhibitory cells of mouse motor cortex (M1) and dendrites of retinal ganglion cells. We show that the learned latent feature space allows for better cell type discrimination than other commonly used features. By sampling new walks from the latent space we can easily construct new morphologies with a specified degree of similarity to their reference neuron, providing an efficient generative model for neural morphologies.

scholarly journals embedGAN: A Method to Embed Images in GAN Latent Space

2021 ◽  
pp. 208-216
Author(s):  
Zhijia Chen ◽  
Weixin Huang ◽  
Ziniu Luo
Keyword(s):  
Interior Design ◽  
Generative Model ◽  
Generation Effect ◽  
Training Time ◽  
Embedded Network ◽  
Latent Space ◽  
Network Application ◽  
Algorithm Framework

AbstractGAN is an efficient generative model. By performing a latent walk in GAN, the generation result can be adjusted. However, the latent walk cannot start from a selected image. The embedGAN is proposed to embed selected images into GAN and remain the generation effect. It contains an embedded network and a generative network. Application cases of residential interior design are given in the article. With advantages of a low computing cost and short training time, embedGAN shows its potential. The embedGAN algorithm framework can be applied to various GANs.

scholarly journals Deep4D: A Compact Generative Representation for Volumetric Video

2021 ◽  
Vol 2 ◽  
Author(s):  
João Regateiro ◽  
Marco Volino ◽  
Adrian Hilton
Keyword(s):  
Generative Model ◽  
Character Animation ◽  
Video Sequences ◽  
Motion Capture Data ◽  
High Quality ◽  
Motion Graphs ◽  
Video Acquisition ◽  
Latent Space ◽  
Multiple Sequences ◽  
Dynamic Shape

This paper introduces Deep4D a compact generative representation of shape and appearance from captured 4D volumetric video sequences of people. 4D volumetric video achieves highly realistic reproduction, replay and free-viewpoint rendering of actor performance from multiple view video acquisition systems. A deep generative network is trained on 4D video sequences of an actor performing multiple motions to learn a generative model of the dynamic shape and appearance. We demonstrate the proposed generative model can provide a compact encoded representation capable of high-quality synthesis of 4D volumetric video with two orders of magnitude compression. A variational encoder-decoder network is employed to learn an encoded latent space that maps from 3D skeletal pose to 4D shape and appearance. This enables high-quality 4D volumetric video synthesis to be driven by skeletal motion, including skeletal motion capture data. This encoded latent space supports the representation of multiple sequences with dynamic interpolation to transition between motions. Therefore we introduce Deep4D motion graphs, a direct application of the proposed generative representation. Deep4D motion graphs allow real-tiome interactive character animation whilst preserving the plausible realism of movement and appearance from the captured volumetric video. Deep4D motion graphs implicitly combine multiple captured motions from a unified representation for character animation from volumetric video, allowing novel character movements to be generated with dynamic shape and appearance detail.

scholarly journals Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces

2019 ◽  
Author(s):  
Jiarui Ding ◽  
Aviv Regev
Keyword(s):  
Single Cell ◽  
Large Datasets ◽  
Generative Model ◽  
Hyperbolic Spaces ◽  
Rna Seq ◽  
Developmental Relationships ◽  
Animal Development ◽  
Latent Space ◽  
Invaluable Tool ◽  
Low Dimensional

AbstractSingle-cell RNA-Seq (scRNA-seq) has become an invaluable tool for studying biological systems in health and diseases. While dimensionality reduction is a crucial step in interpreting the relation between cells based on scRNA-seq, current methods often are hampered by “crowding” of cells in the center of the latent space, biased by batch effects, or inadequately capture developmental relationships. Here, we introduced scPhere, a scalable deep generative model to embed cells into low-dimensional hyperspherical or hyperbolic spaces, as a more accurate representation of the data. ScPhere resolves cell crowding, corrects multiple, complex batch factors, facilitates interactive visualization of large datasets, and gracefully uncovers pseudotemporal trajectories. We demonstrate scPhere on six large datasets in complex tissue from human patients or animal development, demonstrating how it controls for both technical and biological factors and highlights complex cellular relations and biological insights.

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