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 4051–4100 of 5044 papers

TitleStatusHype
Learning Mask Invariant Mutual Information for Masked Image Modeling—0
Learning Minimal Representations with Model Invariance—0
Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling—0
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation—0
Learning neural audio features without supervision—0
Learning Object-Centric Video Models by Contrasting Sets—0
Learning Object Focused Attention—0
Learning Off-Road Terrain Traversability with Self-Supervisions Only—0
Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation—0
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels—0
Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry—0
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation—0
LEARNING PHONEME-LEVEL DISCRETE SPEECH REPRESENTATION WITH WORD-LEVEL SUPERVISION—0
Learning Pixel Trajectories with Multiscale Contrastive Random Walks—0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
Learning Rotation-Equivariant Features for Visual Correspondence—0
Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels—0
Learning Spatial-Semantic Relationship for Facial Attribute Recognition With Limited Labeled Data—0
Learning Speech Representations from Raw Audio by Joint Audiovisual Self-Supervision—0
Learning Symbolic Representations Through Joint GEnerative and DIscriminative Training—0
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture—0
Learning Task-Independent Game State Representations from Unlabeled Images—0
Learning to Customize Text-to-Image Diffusion In Diverse Context—0
Learning to Discover Reflection Symmetry via Polar Matching Convolution—0
Learning to Efficiently Adapt Foundation Models for Self-Supervised Endoscopic 3D Scene Reconstruction from Any Cameras—0
Learning To Explore With Predictive World Model Via Self-Supervised Learning—0
Learning to Generalize One Sample at a Time with Self-Supervision—0
Learning to Identify Physical Parameters from Video Using Differentiable Physics—0
Socially Supervised Representation Learning: the Role of Subjectivity in Learning Efficient Representations—0
Learning to Learn in a Semi-Supervised Fashion—0
Learning to Model the World with Language—0
Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control—0
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
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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