Papers Self-Supervised Learning
“Self-Supervised Learning” 태그가 달린 논문 5,929편 · 필터 해제
Breaking Spurious Correlations via Generative Randomization and Cross-Variant Self-Supervised Learning
Deep neural networks trained with Empirical Risk Minimization (ERM) often fail under distribution shifts because they exploit spurious correlations between object labels and background context. Recent generative approach…
Self-Supervised LearningData AugmentationSTST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning
Brain age - the age inferred from a physiological recording - is an emerging biomarker whose deviation from chronological age tracks neurological and psychiatric burden, and EEG is an attractive substrate for it because …
Self-Supervised LearningGeometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation
Monocular-to-stereo conversion synthesizes stereoscopic content from 2D videos for immersive 3D experiences. In modern Depth-Image-Based Rendering (DIBR) approaches, stereo inpainting of disocclusions is the critical bot…
Self-Supervised LearningVideo GenerationProbing Geospatial SSL Representations with Environmental Signals
Self-supervised learning (SSL) is designed to learn generic, transferable representations rather than representations optimized for a single task. Most geospatial benchmarks evaluate representations solely through downst…
Self-Supervised LearningGraph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes. While commonly treated with neoadjuvant chemotherapy (NACT), e…
Graph Representation LearningSelf-Supervised LearningGraph Neural NetworkMasked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition
Self-supervised learning (SSL) shows strong potential for cross-dataset transfer by improving feature representation and generalization. However, its application to EEG-based emotion recognition remains largely unexplore…
Self-Supervised LearningRepresentation LearningContrastive LearningEmotion RecognitionHASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy
Hierarchical structure is common in image data, where fine-grained clusters often merge into larger, coarser semantic groups. In biological cell images, current self-supervised learning models often suppress this hierarc…
Self-Supervised LearningReduced-Order Models: The Mother of World Models
World models -- compressed latent representations of an environment that support action-conditioned prediction and planning -- are typically presented as a product of modern self-supervised learning. This paper argues th…
Self-Supervised LearningModular Foundation Models for Time-Series Perception in Digital Twins
Engineering Digital Twins and Prognostics and Health Management (PHM) systems rely on robust perception modules to extract actionable information from heterogeneous and non-stationary time-series data. However, most exis…
Self-Supervised LearningFew-Shot LearningUnderstanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data
Recent research has introduced distributed self-supervised learning (D-SSL) approaches to leverage vast amounts of unlabeled decentralized data. However, D-SSL faces the critical challenge of data heterogeneity, and ther…
Self-Supervised LearningContrastive LearningFederated LearningTowards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games
We investigate the problem of learning useful policy representations (embeddings) in two-player zero-sum imperfect-information games. We make three contributions: First, we introduce methods of creating datasets of polic…
Self-Supervised LearningLeVLJEPA: End-to-End Vision-Language Pretraining Without Negatives
Vision-language pretraining remains dominated by contrastive objectives, whereas vision-only self-supervised learning has largely adopted non-contrastive methods. At the same time, the role of vision-language encoders ha…
Self-Supervised LearningSemantic SegmentationMirror-Fusion Attention for Reflection-Aware Self-Supervised Representation Learning
Most self-supervised learning (SSL) methods encourage invariance across augmentations, but strict flip invariance can suppress informative left--right correspondences in approximately bilateral data such as medical image…
Self-Supervised LearningRepresentation LearningLeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning
Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self-supervised learning often depends on view and augmentation choices …
Self-Supervised LearningRepresentation LearningHierarchical Self-Supervised Representation Learning Framework for Multivariate Time Series Grounded in ECG Analysis
Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution. Under such circumstances, self-supervised learning (SSL) met…
Self-Supervised LearningAGE: Adaptive-masking for Graph Embedding in Graph Retrieval-Augmented Generation
GraphRAG is an extension of retrieval-augmented generation (RAG) that supports large language models (LLMs) by referring to graph-structured data as external knowledge. While this technique ideally captures intricate rel…
Self-Supervised LearningGraph EmbeddingA Three-Phase Foundation Model for Tax-Aware Personalized Portfolio Management
We present a three-phase deep reinforcement learning system for personalized portfolio management that addresses three limitations shared by all prior financial RL work: 1) ticker lock-in, 2) monolithic objectives , and …
Self-Supervised LearningReinforcement LearningCLIMB: Centroid-Based Hierarchical Memory for Online Continual Self-Supervised Learning
Online Continual Self-Supervised Learning (OCSSL) aims to learn representations from a continuous stream of unlabeled data, without knowledge of task boundaries and under memory constraints. Existing methods rely either …
Self-Supervised LearningKnowledge DistillationContrastive LearningContinual LearningMitigating Positional Leakage in 3D Masked Autoencoders for Robust Representation Learning
Masked autoencoding has emerged as a prominent paradigm for self-supervised learning on 3D point clouds, achieving competitive performance across downstream tasks. Unlike its 2D counterpart, 3D masked autoencoding direct…
Self-Supervised LearningRepresentation LearningPoint CloudsProgressive Self-Supervised Learning with Individualized Community Assignment for Brain Network Analysis
Brain networks exhibit a modular community structure that varies across individuals and neurological conditions. However, existing self-supervised learning (SSL) methods often overlook this heterogeneity, relying on gene…
Self-Supervised Learning