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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3251–3300 of 5044 papers

TitleStatusHype
Self-Supervised Dialogue Learning—0
Self-supervised Dialogue Learning for Spoken Conversational Question Answering—0
Self-supervised Disentangled Representation Learning—0
Self-Supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images—0
Self-Supervised Domain-Aware Generative Network for Generalized Zero-Shot Learning—0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph—0
SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery—0
Self-Supervised Embeddings for Detecting Individual Symptoms of Depression—0
Self-supervised Facial Action Unit Detection with Region and Relation Learning—0
Self-supervised Feature Enhancement: Applying Internal Pretext Task to Supervised Learning—0
Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots—0
Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences—0
Self-Supervised Feature Learning by Learning to Spot Artifacts—0
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube—0
Self-Supervised Feature Learning from Partial Point Clouds via Pose Disentanglement—0
Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling—0
Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences—0
Self-supervised Graph-based Point-of-interest Recommendation—0
Self-supervised Graph Learning for Occasional Group Recommendation—0
Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks—0
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
Self-supervised Hypergraphs for Learning Multiple World Interpretations—0
Self-supervised Hyperspectral Image Restoration using Separable Image Prior—0
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Self-Supervised Implicit Attention: Guided Attention by The Model Itself—0
Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification—0
Self-Supervised Intensity-Event Stereo Matching—0
Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation—0
Self-Supervised-ISAR-Net Enables Fast Sparse ISAR Imaging—0
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations—0
Self-Supervised Learning Across Domains—0
Self-Supervised Learning Aided Class-Incremental Lifelong Learning—0
Self-supervised Learning and Graph Classification under Heterophily—0
Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease—0
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime—0
Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs—0
Self-Supervised Learning based on Heat Equation—0
Self-Supervised Learning based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation—0
Self-Supervised Learning-Based Path Planning and Obstacle Avoidance Using PPO and B-Splines in Unknown Environments—0
Self-Supervised Learning-Based Source Separation for Meeting Data—0
Self-supervised Learning by View Synthesis—0
Self-Supervised Learning Featuring Small-Scale Image Dataset for Treatable Retinal Diseases Classification—0
Self-supervised Learning for Electroencephalogram: A Systematic Survey—0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
Self-supervised Learning for Acoustic Few-Shot Classification—0
Self-supervised Learning for Anomaly Detection in Computational Workflows—0
Self-supervised Learning for Astronomical Image Classification—0
Self-Supervised Learning for Audio-Based Emotion Recognition—0
Show:102550
← PrevPage 66 of 101Next →
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