SOTAVerified

Self-Supervised Learning

Self-Supervised Learning is proposed for utilizing unlabeled data with the success of supervised learning. Producing a dataset with good labels is expensive, while unlabeled data is being generated all the time. The motivation of Self-Supervised Learning is to make use of the large amount of unlabeled data. The main idea of Self-Supervised Learning is to generate the labels from unlabeled data, according to the structure or characteristics of the data itself, and then train on this unsupervised data in a supervised manner. Self-Supervised Learning is wildly used in representation learning to make a model learn the latent features of the data. This technique is often employed in computer vision, video processing and robot control.

Source: Self-supervised Point Set Local Descriptors for Point Cloud Registration

Image source: LeCun

Papers

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Showing 1601–1650 of 5044 papers

TitleStatusHype
Towards A Universal Graph Structural Encoder—0
Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data—0
InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals—0
Leveraging Large Self-Supervised Time-Series Models for Transferable Diagnosis in Cross-Aircraft Type Bleed Air System—0
Embodied Image Captioning: Self-supervised Learning Agents for Spatially Coherent Image Descriptions—0
JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture—0
The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound—0
Learning Object Focused Attention—0
Impact of Language Guidance: A Reproducibility Study—0
Evolutionary algorithms meet self-supervised learning: a comprehensive survey—0
Dual Deep Learning Approach for Non-invasive Renal Tumour Subtyping with VERDICT-MRI—0
Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems—0
Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach—0
A Self-Supervised Framework for Space Object Behaviour Characterisation—0
Bridging the Gap between Continuous and Informative Discrete Representations by Random Product Quantization—0
Uni4D: A Unified Self-Supervised Learning Framework for Point Cloud Videos—0
Variational Self-Supervised Learning—0
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation—0
Multi-encoder nnU-Net outperforms Transformer models with self-supervised pretraining—0
QIRL: Boosting Visual Question Answering via Optimized Question-Image Relation Learning—0
LV-MAE: Learning Long Video Representations through Masked-Embedding Autoencoders—0
MIMRS: A Survey on Masked Image Modeling in Remote Sensing—0
Towards Computation- and Communication-efficient Computational Pathology—0
Training Frozen Feature Pyramid DINOv2 for Eyelid Measurements with Infinite Encoding and Orthogonal RegularizationCode0
A Plasticity-Aware Method for Continual Self-Supervised Learning in Remote Sensing—0
PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks—0
Learning Velocity and Acceleration: Self-Supervised Motion Consistency for Pedestrian Trajectory Prediction—0
AU-TTT: Vision Test-Time Training model for Facial Action Unit Detection—0
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation—0
Learning Library Cell Representations in Vector Space—0
Masked Self-Supervised Pre-Training for Text Recognition Transformers on Large-Scale Datasets—0
M2D2: Exploring General-purpose Audio-Language Representations Beyond CLAP—0
A Self-Supervised Learning of a Foundation Model for Analog Layout Design Automation—0
Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach—0
Magnitude-Phase Dual-Path Speech Enhancement Network based on Self-Supervised Embedding and Perceptual Contrast Stretch BoostingCode0
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation—0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction—0
Noisier2Inverse: Self-Supervised Learning for Image Reconstruction with Correlated NoiseCode0
Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks—0
Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals—0
An Overview of Low-Rank Structures in the Training and Adaptation of Large Models—0
Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging—0
Self-Supervised Learning based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation—0
Video-XL-Pro: Reconstructive Token Compression for Extremely Long Video Understanding—0
HiRes-FusedMIM: A High-Resolution RGB-DSM Pre-trained Model for Building-Level Remote Sensing Applications—0
Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data—0
PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition—0
Meta-Representational Predictive Coding: Biomimetic Self-Supervised Learning—0
Should we pre-train a decoder in contrastive learning for dense prediction tasks?—0
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All datasetsDABSSTL-10CIFAR10cifar100ImageNet-100 (TEMI Split)TinyImageNetCIFAR-10CIFAR-100CREMA-DTiny ImageNet

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pretraining: NoneImages & Text57.5—Unverified
2Pretraining: ShEDImages & Text54.3—Unverified
3Pretraining: e-MixImages & Text48.9—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy91.7—Unverified
2ResNet18Accuracy91.02—Unverified
3MV-MRAccuracy89.67—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy93.89—Unverified
2ResNet18average top-1 classification accuracy92.58—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy72.51—Unverified
2ResNet18average top-1 classification accuracy69.31—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy82.64—Unverified
2CorInfomax (ResNet18)Top-1 Accuracy80.48—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy51.84—Unverified
2ResNet18average top-1 classification accuracy51.67—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy93.18—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy71.61—Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid BYOL-S/CvTAccuracy67.2—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy54.86—Unverified