Siamese Neural Networks

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Siamese neural networks process two or more inputs through shared or coordinated encoders and learn whether their representations should be close or far apart. Contrastive and triplet losses are common for verification, retrieval, one-shot classification, and matching, while self-supervised variants use paired views to learn general-purpose features. Applications range from medical imaging and biometrics to object tracking, change detection, and scientific data, often where labels are scarce or inputs arrive as pairs over time. A continuing challenge is making learned distances and decision thresholds reliable across datasets and domains, while task-specific designs can outperform generic Siamese architectures.

Latest papers

17 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals
Oct 11, 2026Abdur Rahman, A. A. M. Muzahid, Istiak Ahmed +4Clinical Prediction Multimodal Classification
School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China. · School of Information Technology, Murdoch University, Murdoch, 6150, WA, Australia. · Department of Information and Communication Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.
Brain-PACE: A Deep Siamese MRI Framework for Modelling Longitudinal Brain Acceleration
Sep 10, 2026Samuel Maddox, Jacob Newman, Saber Sami +4Alzheimer's Disease Brain Age Prediction
School of Computing Sciences, University of East Anglia · Norwich Medical School, University of East Anglia
How Far is Too Far? Defining the Distance Threshold for Verification Siamese Networks
Jul 6, 2026Heloísa Dias Viotto, Cauê Samonek, Lucas Garcia Pedroso +3Neural Network Verification Siamese Neural Networks
∗Departamento de Informática (DInf), Universidade Federal do Paraná, Curitiba, PR - Brazil · † Departamento de Matemática (DMAT), Universidade Federal do Paraná Curitiba, PR - Brazil
SiamJEPA: On the Role of Siamese Student Encoders in JEPA
Jul 4, 2026Makoto YamadaJoint-Embedding Predictive Architecture Siamese Neural Networks
Okinawa Institute of Science and Technology
UrbanCDNet: Appearance-Robust and Boundary-Aware Bitemporal Change Detection for Korean Urban Building Monitoring
Jun 29, 2026Abdirashid Omar, Jonghyuk ParkRemote Sensing Change Detection Siamese Neural Networks
Department of Data Science, Graduate School of Kookmin University Seoul 02707, Republic of Korea
Evaluation of Image Matching for Art Skills Assessment
Jun 18, 2026Asaad Alghamdi, Michael Poor, Trung-Nghia Le +1Image Matching Siamese Neural Networks
University of Dayton, Ohio, United States · University of Science, VNU-HCM, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam
A Unified Siamese Learning Framework for Zero-Day Anomaly Detection and Classification in Optical Networks
Jun 9, 2026Carlos Natalino, Flávia P. Monteiro, Paolo MontiFew-Shot Learning Siamese Neural Networks
Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden · Federal University of Western Par´a (UFOPA), 68040-255 Santar´em, Par´a, Brazil
Bridging the Gap Between Natural Language and Market Dynamics via High-Dimensional Representation Learning
May 28, 2026Yujin Jeong, Noelle Jung, Brian Y. C. LeungSentence Embeddings Time Series Forecasting
Mike
Learning Robust and Task-Invariant Functional Representation from fMRI through Siamese Self-Supervised Learning
May 27, 2026Jiyao Wang, Peiyu Duan, Nicha C. Dvornek +4Self-Supervised Learning fMRI Representation Learning
Department of Biomedical Engineering, Yale University, New Haven, CT, USA · Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA · Electrical Engineering, Yale University, New Haven, CT, USA +1
AccLock: Unlocking Identity with Heartbeat Using In-Ear Accelerometers
May 12, 2026Lei Wang, Jiangxuan Shen, Xi Zhang +6Biometric Identification Wearable Sensing
Soochow University, China · Macquarie University, Australia · Aalborg University, Denmark +2
Pseudo-Siamese Network for Planning in Target-Oriented Proactive Dialogues
Apr 4, 2026Xinyue Kang, Maodong Li, Yibin Zheng +1Conversational Agents Task-Oriented Dialogue Systems
School of Computer Science and Technology, Soochow University · Jiangsu Key Lab of Language Computing, Suzhou 215123, China
Maintaining Difficulty: A Margin Scheduler for Triplet Loss in Siamese Networks Training
Mar 27, 2026Roberto Sprengel Minozzo Tomchak, Oge Marques, Lucas Garcia Pedroso +2Metric Learning Margin-Based Learning
Departamento de Informática, Universidade Federal do Paraná, Curitiba, Brazil · College of Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, U.S.A · Departamento de Matemática, Universidade Federal do Paraná, Curitiba, Brazil
Polarization-Based Eye Tracking with Personalized Siamese Architectures
Mar 26, 2026Beyza Kalkanli, Tom Bu, Mahsa Shakeri +4Eye Tracking Gaze Estimation
Meta, Reality Labs, Redmond, WA 98052, USA · Meta, Reality Labs, Burlingame, CA 94010, USA
Scribe Verification in Chinese manuscripts using Siamese, Triplet, and Vision Transformer Neural Networks
Mar 14, 2026Dimitrios-Chrysovalantis Liakopoulos, Yanbo Zhang, Chongsheng Zhang +1Historical Document Analysis Siamese Neural Networks
Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece · School of Computer and Information Engineering, Henan University, Kaifeng, China
Geometric-Topological Perception and Motion Prior for Real-Time Satellite Video Object Tracking
Mar 8, 2026Zixiao Wen, Guangyao Zhou, Jiawei Li +4Visual Object Tracking Siamese Neural Networks
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China · Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing, 100190, China · School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 101408, China
StrokeNeXt: A Siamese-encoder Approach for Brain Stroke Classification in Computed Tomography Imagery
Feb 16, 2026Leo Thomas Ramos, Angel D. SappaMedical Image Classification Classification
Computer Vision Center · Universitat Aut`onoma de Barcelona · ESPOL Polytechnic University
Siamese Neural Network for Label-Efficient Critical Phenomena Prediction in 3D Percolation Models
Jul 5, 2025Shanshan Wang, Dian Xu, Jianmin Shen +3Phase Transitions Siamese Neural Networks
Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, , Wuhan, 430079, Hubei, China · School of Engineering and Technology, Baoshan University, , Baoshan, 678000, Yunnan, China · Ecole Superieure d’Informatique Electronique Automatique, , Ivry-sur-Seine, 94200, , France

Oct 11, 2026J/K move · Enter open · S save

Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals

Abdur Rahman, A. A. M. Muzahid, Istiak Ahmed, Dipto Biswas+3

School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China. · School of Information Technology, Murdoch University, Murdoch, 6150, WA, Australia. · Department of Information and Communication Engineering, Daffodil International University, Dhaka, 1216, Bangladesh.

Clinical Prediction Multimodal Classification Siamese Neural Networks

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Amyotrophic lateral sclerosis (ALS) diagnosis remains challenging due to heterogeneous symptoms and reliance on clinical assessment, underscoring the need for objective, data-driven approaches that leverage patient-generated physiological signals. We propose Siam-ALS, an explainable Siamese deep learning framework that jointly models synchronized electroencephalography (EEG) and eye-tracking (ET) signals. EEG captures ALS-related changes in brain activity, while ET provides information about oculomotor control, allowing the model to learn complementary patterns associated with ALS. The framework employs two modality-specific encoders: a convolutional EEG encoder that captures spatiotemporal neural patterns and an MLP-based ET encoder that models oculomotor dynamics. These modality-specific features are processed through weight-sharing Siamese branches and fused into a compact 64-dimensional joint embedding. A lightweight classifier is used to predict ALS, while pairwise metric learning helps the model learn more effective patterns from the small, diverse ALS datasets. Performance was evaluated on the public EEGET-ALS dataset using strict subject-level cross-validation, where Siam-ALS achieved an accuracy of 0.988 and an AUROC of 0.998, demonstrating strong performance compared with classical machine learning, DL, and non-Siamese fusion approaches. Cross-dataset evaluation on the independent BigP3BCI dataset achieved an AUROC of approximately 0.98, supporting the generalization capability of Siam-ALS across different BCI paradigms. To enhance interpretability, six XAI methods were evaluated in the learned embedding space, with SHAP achieving the highest, though statistically comparable, fidelity in identifying important EEG and ET patterns that contribute to ALS predictions. Overall, Siam-ALS provides an accurate, generalizable, and explainable framework for multimodal ALS detection.