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 3101–3150 of 5044 papers

TitleStatusHype
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models—0
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning—0
Learning Velocity and Acceleration: Self-Supervised Motion Consistency for Pedestrian Trajectory Prediction—0
Learning Video Representations by Transforming Time—0
Learning with Difference Attention for Visually Grounded Self-supervised Representations—0
LEAVES: Learning Views for Time-Series Data in Contrastive Learning—0
LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech—0
Structurally Different Neural Network Blocks for the Segmentation of Atrial and Aortic Perivascular Adipose Tissue in Multi-centre CT Angiography Scans—0
Lesion Search with Self-supervised Learning—0
LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement—0
Less than Few: Self-Shot Video Instance Segmentation—0
Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization—0
Leveraging Auto-Distillation and Generative Self-Supervised Learning in Residual Graph Transformers for Enhanced Recommender Systems—0
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations—0
Leveraging Hidden Structure in Self-Supervised Learning—0
Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms—0
Leveraging Large Self-Supervised Time-Series Models for Transferable Diagnosis in Cross-Aircraft Type Bleed Air System—0
Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis—0
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks—0
Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling—0
Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation—0
Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds—0
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection—0
Leveraging Self-Supervised Learning for Scene Classification in Child Sexual Abuse Imagery—0
Leveraging Self-Supervised Learning Methods for Remote Screening of Subjects with Paroxysmal Atrial Fibrillation—0
Leveraging Semantic Information for Efficient Self-Supervised Emotion Recognition with Audio-Textual Distilled Models—0
Leveraging Superfluous Information in Contrastive Representation Learning—0
Leveraging the Third Dimension in Contrastive Learning—0
Leveraging Time Irreversibility with Order-Contrastive Pre-training—0
Leveraging Uni-Modal Self-Supervised Learning for Multimodal Audio-visual Speech Recognition—0
Lifting Imbalanced Regression with Self-Supervised Learning—0
Lightweight feature encoder for wake-up word detection based on self-supervised speech representation—0
LinBridge: A Learnable Framework for Interpreting Nonlinear Neural Encoding Models—0
Link Prediction with Contextualized Self-Supervision—0
LiRA: Learning Visual Speech Representations from Audio through Self-supervision—0
Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds—0
LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction—0
Liveness Detection in Computer Vision: Transformer-based Self-Supervised Learning for Face Anti-Spoofing—0
LLaSA: Large Language and Structured Data Assistant—0
LLEDA -- Lifelong Self-Supervised Domain Adaptation—0
LLMcap: Large Language Model for Unsupervised PCAP Failure Detection—0
L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction—0
Local Contrastive Feature learning for Tabular Data—0
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation—0
LocalGCL: Local-aware Contrastive Learning for Graphs—0
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning—0
Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation—0
Localizing Memorization in SSL Vision Encoders—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