Search Results - causal adversarial autoencoder

Causal Adversarial Autoencoder for Disentangled SAR Image Representation and Few-Shot Target Recognition by Guo, Qian, Xu, Huilin, Xu, Feng

“… A Causal Adversarial auto-Encoder (CAE) for SAR-ATR is then proposed to embody this disentangled representation, which incorporates a number of novel built-in network features…”
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Information Theoretic Learning-Enhanced Dual-Generative Adversarial Networks With Causal Representation for Robust OOD Generalization by Zhou, Xiaokang, Zheng, Xuzhe, Shu, Tian, Liang, Wei, Wang, Kevin I-Kai, Qi, Lianyong, Shimizu, Shohei, Jin, Qun

“…) and causal representation learning (CRL) in a dual-generative adversarial network (Dual-GAN) architecture, aiming to enhance the robust OOD generalization in modern machine learning…”
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De-Occlusion Face Model based on Deep Occlusor Segmentation and Deep Inpainting Models by Gutierrez, Miguel, Chacon-Murguia, Mario, Ramirez-Quintana, Juan

“… and generative adversarial networks, fundamental challenges persist, such as the causal interpretation of information loss…”
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Causal Discovery and Deep Learning Algorithms for Detecting Geochemical Patterns Associated with Gold-Polymetallic Mineralization: A Case Study of the Edongnan Region by Luo, Zijing, Zuo, Renguang

“… This study investigated the application of a causal discovery algorithm and deep learning models to identify geochemical anomaly patterns associated with mineralization…”
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DAG-AVAE: Combining GAN with Adversarial VAE to Enhance Causal Structure Learning by Jiang, Tao, Cai, Qingsong, Jia, Ming, Cai, Xuanzhi

“…The task of uncovering the causal structure underlying observed data has garnered significant interest and posed considerable challenges in recent years…”
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Counterfactual AI in Healthcare: Enhancing Decision-Making and Outcome Prediction by Sharma, Harsh, Pradhan, Rajeshwari, Mogha, Harsh, Singh, Shivpratap, Kumar, Rupesh

“…), Variational Autoencoders (VAEs), and Structural Causal Models (SCMs), to estimate treatment effects accurately…”
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DR-VIDAL - Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data by Ghosh, Shantanu, Feng, Zheng, Bian, Jiang, Butler, Kevin, Prosperi, Mattia

“… DR-VIDAL integrates: (i) a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; (ii…”
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Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM by Liu, Xin, Yan, Shengyuan, Zhang, Xiaodan

“… The proposed method comprises three primary aspects: 1) a regularized autoencoder for the denoising and feature extraction of expert evaluation results, 2…”
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Counterfactual Inference for Generalized Zero-Shot Compound-Fault Diagnosis by Xu, Juan, Kong, Hui, Ding, Xu, Yuan, Xiaohui

“… This a typically true for fault diagnosis in machinery, particularly for compound faults. The counterfactual inference reveals the causal components inherent in the fault data…”
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A WGAN-based Missing Data Causal Discovery Method by Gao, Yanyang, Cai, Qingsong

“…The state-of-the-art causal discovery algorithms are typically based on complete observed data…”
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Fairness without the sensitive attribute via Causal Variational Autoencoder by Grari, Vincent, Lamprier, Sylvain, Detyniecki, Marcin

“… Based on a causal graph, we rely on a new variational auto-encoding based framework named SRCVAE to infer a sensitive information…”
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Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM by Xin Liu, Shengyuan Yan, Xiaodan Zhang

“… The proposed method comprises three primary aspects: 1) a regularized autoencoder for the denoising and feature extraction of expert evaluation results, 2…”
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Using Causality-Driven Graph Representation Learning for APT Attacks Path Identification by Cheng, Xiang, Kuang, Miaomiao, Yang, Hongyu

“…In the cybersecurity attack and defense space, the “attacker” and the “defender” form a dynamic and symmetrical adversarial pair…”
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Algorithmic Recourse in Sequential Decision-Making for Long-Term Fairness by Gumucio, Francisco

“…Algorithmic decision-making systems are increasingly being deployed in high-stakes domains such as criminal justice, education, and financial services. While…”
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Information Theoretic Learning-Enhanced Dual-Generative Adversarial Networks With Causal Representation for Robust OOD Generalization by Xiaokang Zhou, Xuzhe Zheng, Tian Shu, Wei Liang, Kevin I-Kai Wang, Lianyong Qi, Shohei Shimizu, Qun Jin

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A transformer guided multi modal learning framework for predictive and causal assessment of thermal runaway in high energy batteries by Gajghate, Sameer Sheshrao, Noor, Muhamad Mat, Kumar, Subhash, Bansod, Premendra Janardan, Shelare, Sagar Dnyaneshwar, Nikam, Keval Chandrakant, Jathar, Laxmikant Dattatray, Dennison, Milon Selvam

“…) FUSE-GEN, adversarial trained dual-encoder variational autoencoder, fusing acoustic emission (AE…”
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DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data by Ghosh, Shantanu, Zheng, Feng, Bian, Jiang, Butler, Kevin, Prosperi, Mattia

“… DR-VIDAL integrates: (i) a variational autoencoder (VAE) to factorize confounders into latent variables according to causal assumptions; (ii…”
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Constraint-Driven Causal Representation Learning for Vigilance Robust Estimation in Brain-Computer Interface by Zhang, Xuan, Zheng, Wang, Li, Zhigang, Yang, Yi, Liu, Weijia, Cai, Hongxin, Zhu, Junru, Liu, Jingyu, Hu, Bin, Dong, Qunxi

“…, out-of-distribution (OOD) scenarios. The core idea of this study is to learn constraints that capture causal information from the input based on the assumed underlying data generating process…”
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A Novel Causal Federated Transfer Learning Method for Power Transformer Fault Diagnosis Based on Voiceprint Recognition by Zhang, Kai, Lu, Hongming, Han, Shuai, Zhao, Xin

“… First, a causal FTL framework is proposed by integrating a causal graph autoencoder into FTL to capture nonlinear causal features between voiceprint features and faults…”
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Integrating causal representations with domain adaptation for fault diagnosis by Jiang, Ming, Zhou, Kuang, Gao, Jiahui, Zhang, Fode

“… In this paper, a Cross-domain Fault Diagnosis model based on Causal Representation learning (CFDCR) is proposed. This method employs causal representation learning…”
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