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A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt
Recently, ChatGPT, along with DALL-E-2 and Codex, has been gaining significant attention
from society. As a result, many individuals have become interested in related resources and …
from society. As a result, many individuals have become interested in related resources and …
Deep clustering: A comprehensive survey
Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
a good data representation is crucial for clustering algorithms. Recently, deep clustering …
Lion: Latent point diffusion models for 3d shape generation
Denoising diffusion models (DDMs) have shown promising results in 3D point cloud
synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high …
synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high …
Infogcn: Representation learning for human skeleton-based action recognition
Human skeleton-based action recognition offers a valuable means to understand the
intricacies of human behavior because it can handle the complex relationships between …
intricacies of human behavior because it can handle the complex relationships between …
Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models
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 …
the distribution of training samples. Research has fragmented into various interconnected …
[فهرست منابع][C] An introduction to variational autoencoders
An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …
Deep semi-supervised anomaly detection
Deep approaches to anomaly detection have recently shown promising results over shallow
methods on large and complex datasets. Typically anomaly detection is treated as an …
methods on large and complex datasets. Typically anomaly detection is treated as an …
Isolating sources of disentanglement in variational autoencoders
We decompose the evidence lower bound to show the existence of a term measuring the
total correlation between latent variables. We use this to motivate the beta-TCVAE (Total …
total correlation between latent variables. We use this to motivate the beta-TCVAE (Total …
Variational autoencoders for collaborative filtering
We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback.
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of …
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of …
Disentangling by factorising
We define and address the problem of unsupervised learning of disentangled
representations on data generated from independent factors of variation. We propose …
representations on data generated from independent factors of variation. We propose …