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Synthetic data in human analysis: A survey
Deep neural networks have become prevalent in human analysis, boosting the performance
of applications, such as biometric recognition, action recognition, as well as person re …
of applications, such as biometric recognition, action recognition, as well as person re …
xViTCOS: explainable vision transformer based COVID-19 screening using radiography
Objective: Since its outbreak, the rapid spread of COrona VIrus Disease 2019 (COVID-19)
across the globe has pushed the health care system in many countries to the verge of …
across the globe has pushed the health care system in many countries to the verge of …
Ard-vae: A statistical formulation to find the relevant latent dimensions of variational autoencoders
The variational autoencoder (VAE) is a popular, deep, latent-variable model (DLVM) due to
its simple yet effective formulation for modeling the data distribution. Moreover, optimizing …
its simple yet effective formulation for modeling the data distribution. Moreover, optimizing …
FlexAE: Flexibly learning latent priors for wasserstein auto-encoders
Auto-Encoder (AE) based neural generative frameworks model the joint-distribution
between the data and the latent space using an Encoder-Decoder pair, with regularization …
between the data and the latent space using an Encoder-Decoder pair, with regularization …
Adaptive compression of the latent space in variational autoencoders
Abstract Variational Autoencoders (VAEs) are powerful generative models that have been
widely used in various fields, including image and text generation. However, one of the …
widely used in various fields, including image and text generation. However, one of the …
DisFormer: Disentangled Object Representations for Learning Visual Dynamics Via Transformers
We focus on the task of visual dynamics prediction. Recent work has shown that object-
centric representations can greatly help improve the accuracy of learning such dynamics in …
centric representations can greatly help improve the accuracy of learning such dynamics in …
Sparsity driven latent space sampling for generative prior based compressive sensing
We address the problem of recovering signals from compressed measurements based on
generative priors. Recently, generative-model based compressive sensing (GMCS) methods …
generative priors. Recently, generative-model based compressive sensing (GMCS) methods …
RENs: Relevance Encoding Networks
The manifold assumption for high-dimensional data assumes that the data is generated by
varying a set of parameters obtained from a low-dimensional latent space. Deep generative …
varying a set of parameters obtained from a low-dimensional latent space. Deep generative …
scRAE: deterministic regularized autoencoders with flexible priors for clustering single-cell gene expression data
Clustering single-cell RNA sequence (scRNA-seq) data poses statistical and computational
challenges due to their high-dimensionality and data-sparsity, also known as …
challenges due to their high-dimensionality and data-sparsity, also known as …
A machine learning approach for fighting the curse of dimensionality in global optimization
Finding global optima in high-dimensional optimization problems is extremely challenging
since the number of function evaluations required to sufficiently explore the search space …
since the number of function evaluations required to sufficiently explore the search space …