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Autoencoders [article]

Dor Bank, Noam Koenigstein, Raja Giryes
2021 arXiv   pre-print
This chapter surveys the different types of autoencoders that are mainly used today. It also describes various applications and use-cases of autoencoders.  ...  An autoencoder is a specific type of a neural network, which is mainly designed to encode the input into a compressed and meaningful representation, and then decode it back such that the reconstructed  ...  Use of autoencoders for recommendation systems A recommender system, is a model or system that seek to predict users preferences or affinities to items [41] .  ... 
arXiv:2003.05991v2 fatcat:qs45nliutfc4zmkfpzditgn464

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [article]

Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
2020 arXiv   pre-print
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.  ...  filter untruthful noises (e.g., spammers and fake information) or enhance attack resistance; explainable recommender systems that provide explanations of recommended items.  ...  The generative learning for recommender systems is majorly using generative models, e.g. variational autoencoders (VAE) and generative adversarial neural network (GAN), for producing the potential ratings  ... 
arXiv:2004.03774v2 fatcat:q7mehir7hbbzpemw3q5fkby5ty

Adversarial Mobility Learning for Human Trajectory Classification

Qiang Gao, Fengli Zhang, Fuming Yao, Ailing Li, Lin Mei, Fan Zhou
2020 IEEE Access  
INDEX TERMS Trajectory user linking, adversarial model, autoencoder, attention mechanism, human mobility.  ...  In this work, we present a novel semi-supervised method, called AdattTUL, to make adversarial mobility learning for human trajectory classification, which is an end-to-end framework modeling human moving  ...  Xu et al. propose a semi-supervised sequential variational autoencoder framework by using conditional RNN for text classification.  ... 
doi:10.1109/access.2020.2968935 fatcat:qtoljzt3ircstncdwsictuofae

Deep Learned Frame Prediction for Video Compression [article]

Serkan Sulun
2018 arXiv   pre-print
This proves that even though adversarial training is useful for generating video frames that are more pleasing to the human eye, they should not be employed for video compression.  ...  For frame prediction, we compare our method with the baseline methods of frame difference and 16x16 block motion compensation.  ...  Variational Autoencoders Variational Autoencoders [3] are capable of creating novel outputs that are similar to the input data.  ... 
arXiv:1811.10946v1 fatcat:aviqwcrrrrfxdf5ez75ooeueq4

Can Learned Frame-Prediction Compete with Block-Motion Compensation for Video Coding? [article]

Serkan Sulun, A. Murat Tekalp
2020 arXiv   pre-print
The implications of training with L1, L2, or combined L2 and adversarial loss on prediction performance and compression efficiency are analyzed.  ...  Given recent advances in learned video prediction, we investigate whether a simple video codec using a pre-trained deep model for next frame prediction based on previously encoded/decoded frames without  ...  Variational autoencoders employ KL-divergence [23] , [38] , [37] , [19] and GANs employ adversarial loss [3] , [19] , [12] .  ... 
arXiv:2007.08922v1 fatcat:cgdznu6qyfanvnpcbo73rqnh6i

Deep Learning based Recommender System: A Survey and New Perspectives [article]

Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay
2018 arXiv   pre-print
The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research.  ...  More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art.  ...  Almost all the autoencoder variants such as denoising autoencoder, variational autoencoder, contactive autoencoder and marginalized autoencoder can be applied to recommendation task.  ... 
arXiv:1707.07435v6 fatcat:2q2dbfy2jvdydhbrmmbyrzctnq

A Survey on Face Data Augmentation [article]

Xiang Wang and Kai Wang and Shiguo Lian
2019 arXiv   pre-print
We present their principles, discuss the results and show their applications as well as limitations. Different evaluation metrics for evaluating these approaches are also introduced.  ...  Among all these approaches, we put the emphasis on the deep learning-based works, especially the generative adversarial networks which have been recognized as more powerful and effective tools in recent  ...  Among them, the three most popular models are Autoregressive Models, Variational Autoencoders (VAEs), and Generative Adversarial Networks.  ... 
arXiv:1904.11685v1 fatcat:phcwwc7gcfablgytt6itr6xade

A Comprehensive Survey on Community Detection with Deep Learning [article]

Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
2021 arXiv   pre-print
The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.  ...  Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages  ...  [122] Variational graph auto-encoders Variational Graph Autoencoder for VGAECD Community Detection [125] Learning community structure with variational autoencoder Optimizing Variational Graph Autoencoder  ... 
arXiv:2105.12584v2 fatcat:matipshxnzcdloygrcrwx2sxr4

Deep Variational Models for Collaborative Filtering-based Recommender Systems [article]

Jesús Bobadilla, Fernando Ortega, Abraham Gutiérrez, Ángel González-Prieto
2021 arXiv   pre-print
and structured latent spaces that variational autoencoders exhibit.  ...  On the other hand, data augmentation through variational autoencoder does not provide accurate results in the collaborative filtering field due to the high sparsity of recommender systems.  ...  In contrast with the autoencoder and Generative Adversarial Network (GAN) approaches in the CF field (Gao et al., 2021) , we shall not use the generative decoder stage and we maintain the regression output  ... 
arXiv:2107.12677v1 fatcat:eizxftcabzfirp66tgphrl7oai

ColdGAN: Resolving Cold Start User Recommendation by using Generative Adversarial Networks [article]

Po-Lin Lai, Chih-Yun Chen, Liang-Wei Lo, Chien-Chin Chen
2020 arXiv   pre-print
Mitigating the new user cold-start problem has been critical in the recommendation system for online service providers to influence user experience in decision making which can ultimately affect the intention  ...  Previous studies leveraged various side information from users and items; however, it may be impractical due to privacy concerns.  ...  In contrast, we leverage GAN and time-based rejuvenation to generate more plausible and personalize ratings for cold-start users. Table 4 .  ... 
arXiv:2011.12566v1 fatcat:spf224yaqndgrcadeuz3x2hqwe

Learning Representations of Natural Language Texts with Generative Adversarial Networks at Document, Sentence, and Aspect Level

Aggeliki Vlachostergiou, George Caridakis, Phivos Mylonas, Andreas Stafylopatis
2018 Algorithms  
for the learning representation of natural language, both in supervised and unsupervised settings at the document, sentence, and aspect level.  ...  Generative adversarial network (GAN) approaches have shown impressive results in producing generative models of images, but relatively little work has been done on evaluating the performance of these methods  ...  Generative Adversarial Networks for NLP Tasks In this section, we review recent research on discovering rich structure in natural language with variational autoencoders (VAEs) [3] and GANs [4] .  ... 
doi:10.3390/a11100164 fatcat:42tmwzgyorc4je42ym2xvbfpcy

Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey [article]

Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley
2021 arXiv   pre-print
Finally, we explain the autoencoders based on adversarial learning including adversarial autoencoder, PixelGAN, and implicit autoencoder.  ...  Then, maximum likelihood estimation in GAN are explained along with f-GAN, adversarial variational Bayes, and Bayesian GAN.  ...  In contrast to variational autoencoder (Kingma & Welling, 2014; Ghojogh et al., 2021a) which uses KL divergence and evidence lower bound, AAE uses adversarial learning for imposing a specific distribution  ... 
arXiv:2111.13282v1 fatcat:xakdafbdxvedtflpe7codyztji

Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective [article]

Gabriel Resende Machado, Eugênio Silva, Ronaldo Ribeiro Goldschmidt
2020 arXiv   pre-print
Here, novel taxonomies for categorizing adversarial attacks and defenses are introduced and discussions about the existence of adversarial examples are provided.  ...  Further, in contrast to exisiting surveys, it is also given relevant guidance that should be taken into consideration by researchers when devising and evaluating defenses.  ...  For further orientations, it is recommended consult the authors' paper [19] .  ... 
arXiv:2009.03728v1 fatcat:ysprss2tebcwrh4agv73v2mbpy

Review of Disentanglement Approaches for Medical Applications – Towards Solving the Gordian Knot of Generative Models in Healthcare [article]

Jana Fragemann, Lynton Ardizzone, Jan Egger, Jens Kleesiek
2022 arXiv   pre-print
In this paper, we give a comprehensive overview of popular generative models, like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Flow-based Models.  ...  After introducing the theoretical frameworks, we give an overview of recent medical applications and discuss the impact and importance of disentanglement approaches for medical applications.  ...  Latent Space Generator Generated Data Real Data Discriminator Probability: Input was real or generated Variational Autoencoder Like Generative Adversarial Networks, Variational Autoencoders consist of  ... 
arXiv:2203.11132v1 fatcat:fxrniu6dtjcz5cumwientkqh7i

Guided Dialog Policy Learning without Adversarial Learning in the Loop [article]

Ziming Li, Sungjin Lee, Baolin Peng, Jinchao Li, Julia Kiseleva, Maarten de Rijke, Shahin Shayandeh, Jianfeng Gao
2020 arXiv   pre-print
Reinforcement Learning (RL) methods have emerged as a popular choice for training an efficient and effective dialogue policy.  ...  To overcome the listed issues, we propose to decompose the adversarial training into two steps.  ...  We also develop a variant DQN(GAN-AE) by replacing the variational autoencoder in DQN(GAN-VAE) with an vanilla autoencoder.  ... 
arXiv:2004.03267v2 fatcat:vddneggvqzhbjg3cl2peg2imyu
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