Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning by Josue Genaro Almaraz-Rivera, Jose Antonio Cantoral-Ceballos, Juan Felipe Botero, Francisco Javier MunOz, Brian David Martinez

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Text Geolocation Prediction via Self-Supervised Learning by Yuxing Wu, Zhuang Zeng, Kaiyue Liu, Zhouzheng Xu, Yaqin Ye, Shunping Zhou, Huangbao Yao, Shengwen Li

“…To address this limitation, this paper presents a method for text geolocation prediction without labeled samples, namely GeoSG (Geographic Self-Supervised Geolocation) model, which leverages self-supervised learning to improve text geolocation prediction in situations where labeled samples are unavailable. …”
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Self-supervised feature learning for acoustic data analysis by Ahmet Pala, Anna Oleynik, Ketil Malde, Nils Olav Handegard

“…For this purpose, we adopt a self-supervised method inspired by the Self DIstillation with NO Labels (DINO) model. …”
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Efficient Onboard Multitask AI Architecture Based on Self-Supervised Learning by Gabriele Inzerillo, Diego Valsesia, Enrico Magli

“…There is growing interest toward the use of artificial intelligence (AI) directly onboard satellites for quick analysis and rapid response to critical events such as natural disasters. This article presents a blueprint to the mission designer for the development of a modular and efficient deep learning payload to address multiple onboard inference tasks. …”
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Attentive Self-supervised Contrastive Learning (ASCL) for plant disease classification by Getinet Yilma, Mesfin Dagne, Mohammed Kemal Ahmed, Ravindra Babu Bellam

“…We propose an Attentive Self-supervised Contrastive Learning (ASCL) framework that leverages transferable representations as supervision signals. …”
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DASFormer: self-supervised pretraining for earthquake monitoring by Qianggang Ding, Zhichao Shen, Weiqiang Zhu, Bang Liu

“…In this paper, we present DASFormer, a novel self-supervised pretraining technique on DAS data with a coarse-to-fine framework that models spatial-temporal signal correlation. …”
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CODE CLONE DETECTION WITH SELF-SUPERVISION ON DUAL GRAPHS by Chunguang Li, Adisorn Sirikham, Jessada Konpang, Yan Wang

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Self-supervised learning reduces label noise in sharp wave ripple classification by Saber Graf, Pierre Meyrand, Cyril Herry, Tiaza Bem, Feng-Sheng Tsai

“…Addressing this challenge, our study innovatively applies self-supervised learning (SSL) for the classification of sharp wave ripples (SWRs), high-frequency oscillations involved in memory processing that were generated before or after the encoding of spatial information. …”
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Self-supervised multi-stage deep learning network for seismic data denoising by Omar M. Saad, Matteo Ravasi, Tariq Alkhalifah

“…However, finding an optimal balance between preserving seismic signals and effectively reducing seismic noise presents a substantial challenge. In this study, we introduce a multi-stage deep learning model, trained in a self-supervised manner, designed specifically to suppress seismic noise while minimizing signal leakage. …”
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Masked Modeling-Based Ultrasound Image Classification via Self-Supervised Learning by Kele Xu, Kang You, Boqing Zhu, Ming Feng, Dawei Feng, Cheng Yang

“…In this paper, drawing inspiration from self-supervised learning techniques, we present a pre-training method based on mask modeling specifically designed for ultrasound data. …”
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Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series by Iris Dumeur, Silvia Valero, Jordi Inglada

“…In this article, a new self-supervised strategy for learning meaningful representations of complex optical satellite image time series (SITS) is presented. …”
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Accelerated diffusion tensor imaging with self-supervision and fine-tuning by Phillip Martin, Diego Martin, Maria Altbach, Ali Bilgin

“…We propose a self-supervised deep learning with fine-tuning (SSDLFT) framework to reduce training data requirements. …”
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SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping by Samuel Garske, Konrad Heidler, Bradley Evans, Kee Choon Wong, Xiao Xiang Zhu

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Person Re-Identification With Self-Supervised Teacher for In-Box Noise by Yonghyeok Seo, Seung-Hun Kim

“…To address this in-box noise, we propose a methodology that involves training with a self-supervised teacher model. This approach exploits the relevant identity information within the box-identity pair, enabling parallel learning with the main re-identification task and interpreting the critical identity areas in the image as guided by the teacher. …”
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Contrastive Self-Supervised Learning for Sensor-Based Human Activity Recognition: A Review by Hui Chen, Charles Gouin-Vallerand, Kevin Bouchard, Sebastien Gaboury, Melanie Couture, Nathalie Bier, Sylvain Giroux

“…The empirical performance comparisons of different methods are presented on benchmark datasets in linear evaluation, semi-supervised learning, and transfer learning scenarios. …”
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Self‐Supervised Pre‐Training and Few‐Shot Finetuning for Gas‐Bearing Prediction by Long Han, Xinming Wu, Renjie Chen, Yunhua Shi, Zhanxuan Hu, Huijing Fang

“…This paper presents a deep learning workflow for directly predicting gas saturation from multiple seismic attribute data, involving pre‐training on large‐scale unlabeled data and finetuning with few labeled data to address the few‐shot challenge. …”
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A Novel MoCo-Based Self-Supervised Learning Framework for Solar Panel Defect Detection by Jun Huang, Shamsul Arrieya Ariffin, Yongqiang Chen, Jinghui Lin, Wanting Xu

“…Defect detection in solar panels remains constrained by the limitations of manual labeling and the inefficiency of traditional inspection methods, which often struggle with large, high-resolution imagery. This study presents a novel self-supervised learning approach using the Momentum Contrast (MoCo) framework to address these challenges without relying on annotated datasets. …”
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Self-supervised learning analysis of multi-FISH labeled cell-type map in thick brain slices by Weijie Zheng, Weijie Zheng, Yiping An, Yiping An, Kang Li, Jinyue Wang, Jianqing Gao, Huawei Mu, Huawei Mu, Jin Tang, Jin Tang, Hao Wang, Hao Wang

“…VUSMamba employs contrastive learning and pretext tasks for self-supervised learning on unlabeled data, followed by fine-tuning with minimal annotations. …”
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Self-Supervised Learning of End-to-End 3D LiDAR Odometry for Urban Scene Modeling by Shuting Chen, Zhiyong Wang, Chengxi Hong, Yanwen Sun, Hong Jia, Weiquan Liu

“…While deep learning offers solutions, current approaches for point cloud alignment exhibit key limitations: self-supervised losses often neglect inherent alignment uncertainties, and supervised methods require costly pixel-level correspondence annotations. …”
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Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network by Shengwei Qin, Yao Jin, Hailong Hu

“…To address these concerns, we present SSPU-FENet, a self-supervised network consisting of two modules specifically designed for geometric detail-preserved point cloud upsampling. …”
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