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Neural Topic Modeling with Cycle-Consistent Adversarial Training [article]

Xuemeng Hu, Rui Wang, Deyu Zhou, Yuxuan Xiong
2020 arXiv   pre-print
To overcome such limitations, we propose Topic Modeling with Cycle-consistent Adversarial Training (ToMCAT) and its supervised version sToMCAT.  ...  The recently proposed Adversarial-neural Topic Model models topics with an adversarially trained generator network and employs Dirichlet prior to capture the semantic patterns in latent topics.  ...  To address such limitations of ATM, we propose a novel neural topic modeling approach, named Topic Modeling with Cycle-consistent Adversarial Training (ToMCAT).  ... 
arXiv:2009.13971v1 fatcat:humpi53tfbbprf3pq2pizek4nu

Neural Topic Modeling with Cycle-Consistent Adversarial Training

Xuemeng Hu, Rui Wang, Deyu Zhou, Yuxuan Xiong
2020 Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)   unpublished
To overcome such limitations, we propose Topic Modeling with Cycle-consistent Adversarial Training (ToMCAT) and its supervised version sToMCAT.  ...  The recently proposed Adversarial-neural Topic Model models topics with an adversarially trained generator network and employs Dirichlet prior to capture the semantic patterns in latent topics.  ...  To address such limitations of ATM, we propose a novel neural topic modeling approach, named Topic Modeling with Cycle-consistent Adversarial Training (ToMCAT).  ... 
doi:10.18653/v1/2020.emnlp-main.725 fatcat:2qqtw7w26fbrdi2uasrt7jyyaa

Cycle-consistent Generative Adversarial Networks for Neural Style Transfer using data from Chang'E-4 [article]

J. de Curtó, R. Duvall
2020 arXiv   pre-print
We introduce tools to handle planetary data from the mission Chang'E-4 and present a framework for Neural Style Transfer using Cycle-consistency from rendered images.  ...  Generative Adversarial Networks (GANs) have had tremendous applications in Computer Vision. Yet, in the context of space science and planetary exploration the door is open for major advances.  ...  Cycle-consistent Generative Adversarial Networks Our focus here is on Cycle-consistent Generative Adversarial Networks , where we work on unpaired image-to-image translation [Park et al. 2020 ].  ... 
arXiv:2011.11627v1 fatcat:uu4z2i64xfepjn2axc2msvsmsm

Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer [article]

Yufang Huang, Wentao Zhu, Deyi Xiong, Yiye Zhang, Changjian Hu, Feiyu Xu
2020 arXiv   pre-print
In this paper, we propose a novel neural approach to unsupervised text style transfer, which we refer to as Cycle-consistent Adversarial autoEncoders (CAE) trained from non-parallel data.  ...  The entire CAE with these three components can be trained end-to-end.  ...  When we disable the cycle-consistent constraint, we can train the model successfully.  ... 
arXiv:2010.00735v1 fatcat:wdvglp64mng5jcoozng7pn5koq

Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews [article]

Runcong Zhao and Lin Gui and Gabriele Pergola and Yulan He
2021 arXiv   pre-print
BTM is built on the Poisson factorisation model with the incorporation of adversarial learning. It has been evaluated on a dataset constructed from Amazon reviews.  ...  Different from existing models for sentiment-topic extraction which assume topics are grouped under discrete sentiment categories such as 'positive', 'negative' and 'neural', BTM is able to automatically  ...  Similarly, builds on the aforementioned adversarial approach adding cycle-consistent constraints.  ... 
arXiv:2101.10150v1 fatcat:hyxyreo5cnenvnwczqil57tqgm

Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks [article]

Keval Doshi
2019 arXiv   pre-print
In this literature, we use a deep convolutional neural network with SVM as a classifier to help with recognizing the state of a cooking object.  ...  Object detection and recognition has been an ongoing research topic for a long time in the field of computer vision.  ...  Because this mapping is highly under-constrained, it is coupled with an inverse mapping F : Y → X and a cycle consistency loss is introduced to push F (G(X))X (and vice versa) [5] . IV.  ... 
arXiv:1907.13576v1 fatcat:ckyvrrvmwzcsff22gxsvc7bs2a

A Survey of Deep Learning-Based Source Image Forensics

Pengpeng Yang, Daniele Baracchi, Rongrong Ni, Yao Zhao, Fabrizio Argenti, Alessandro Piva
2020 Journal of Imaging  
In this survey, we present the most important data-driven algorithms that deal with the problem of image source forensics.  ...  To make order in this vast field, we have divided the area in five sub-topics: source camera identification, recaptured image forensic, computer graphics (CG) image forensic, GAN-generated image detection  ...  [111] proposed a Cycle-GAN-based scheme by fusing the adversarial loss, the cycle consistency loss and the low frequency consistency loss.  ... 
doi:10.3390/jimaging6030009 pmid:34460606 pmcid:PMC8321025 fatcat:sv5pucjdqffexexdwlrxq4jlni

Contrastive Learning for Neural Topic Model [article]

Thong Nguyen, Anh Tuan Luu
2021 arXiv   pre-print
of neural topic model.  ...  Recent empirical studies show that adversarial topic models (ATM) can successfully capture semantic patterns of the document by differentiating a document with another dissimilar sample.  ...  Adversarial Topic Model [4] is a topic modeling approach that models the topics with GAN-based architecture.  ... 
arXiv:2110.12764v1 fatcat:2oz4s2hfnvdjbpne6inqclpufa

Topology and geometry of data manifold in deep learning [article]

German Magai, Anton Ayzenberg
2022 arXiv   pre-print
In addition, we consider the issue of the geometry of adversarial attacks in the classification task and spoofing attacks on face recognition systems.  ...  Despite significant advances in the field of deep learning in applications to various fields, explaining the inner processes of deep learning models remains an important and open question.  ...  This assumption applies to both neural networks and brain models and also consistent with findings from neuroscience [71] .  ... 
arXiv:2204.08624v1 fatcat:silqzmxqmzchjpuwliuiq3vqty

EventGAN: Leveraging Large Scale Image Datasets for Event Cameras [article]

Alex Zihao Zhu, Ziyun Wang, Kaung Khant, Kostas Daniilidis
2019 arXiv   pre-print
We train this network on pairs of images and events, using an adversarial discriminator loss and a pair of cycle consistency losses.  ...  The cycle consistency losses utilize a pair of pre-trained self-supervised networks which perform optical flow estimation and image reconstruction from events, and constrain our network to generate events  ...  Both cycle consistency networks share the same architecture as the generator network, with the losses applied each time the generator is updated in the adversarial framework.  ... 
arXiv:1912.01584v2 fatcat:sm6j7nenjba6vnezexx47na56y

Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

Stephen Dankwa, Lu Yang
2021 Electronics  
Therefore, this study proposed computation-efficient deep learning with a mixed batch adversarial generation process model, which attempted to break the transferability attack, and mitigate the problem  ...  After performing K-fold cross-validation, experimental results showed that the proposed defense model achieved mean accuracies in the range of 82–84% among three gradient-based adversarial attack datasets  ...  Moreover, as a result, improving the robustness of deep learning models against adversarial attacks has been a popular topic among researchers.  ... 
doi:10.3390/electronics10151798 fatcat:e4khz6abfvglteflhgnvzwrbiy

CycleGAN-Based Emotion Style Transfer as Data Augmentation for Speech Emotion Recognition

Fang Bao, Michael Neumann, Ngoc Thang Vu
2019 Interspeech 2019  
Cycle consistent adversarial networks (CycleGAN) have shown great success in image style transfer with unpaired datasets.  ...  training set.  ...  In addition, a CycleGAN regularizes the adversarial training with a cycle consistency loss.  ... 
doi:10.21437/interspeech.2019-2293 dblp:conf/interspeech/BaoNV19 fatcat:33s7yiwsdzfhlpju35dkmn72um

Russian Natural Language Generation: Creation of a Language Modelling Dataset and Evaluation with Modern Neural Architectures [article]

Zein Shaheen, Gerhard Wohlgenannt, Bassel Zaity, Dmitry Mouromtsev, Vadim Pak
2020 arXiv   pre-print
In this work, we i) provide a novel reference dataset for Russian language modeling, ii) experiment with popular modern methods for text generation, namely variational autoencoders, and generative adversarial  ...  networks, which we trained on the new dataset.  ...  It starts with β = 0 and gradually increases during training to β = 1. • Cyclical Annealing Schedule: split the training process into M cycles, each cycle consists of two stages: 1.  ... 
arXiv:2005.02470v1 fatcat:3urzycxiubdtdnkzxmwpv37gym

MDEA: Malware Detection with Evolutionary Adversarial Learning [article]

Xiruo Wang, Risto Miikkulainen
2020 arXiv   pre-print
By retraining the model with the evolved malware samples, its performance improves a significant margin.  ...  These applications take in raw or processed binary data to neural network models to classify as benign or malicious files.  ...  This paper proposes MDEA, an Adversarial Malware Detection model that combines a deep neural network with an evolutionary optimization at its training.  ... 
arXiv:2002.03331v2 fatcat:g2nxt2gvufhdzpbnbzgvyb7tye

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data [article]

Eva Brombacher, Maren Hackenberg, Clemens Kreutz, Harald Binder, Martin Treppner
2022 bioRxiv   pre-print
In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns via a joint embedding.  ...  [60] , scAEGAN [24] also embraces the concept of cycle consistency, integrating the adversarial training mechanism of a cycle GAN [66] into an autoencoder framework.  ...  adversarial loss and latent cycle-consistency loss VAE VAE with semi-supervised cross-domain translation VAE (product of experts) AE+GAN: adversarial discriminators on la- tent spaces Multimodal VAE with  ... 
doi:10.1101/2022.06.06.494951 fatcat:nwbitt4a2zg4fiymreckvazfly
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