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 3651–3700 of 5044 papers

TitleStatusHype
SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis—0
SSL Framework for Causal Inconsistency between Structures and Representations—0
SSL-NBV: A Self-Supervised-Learning-Based Next-Best-View algorithm for Efficient 3D Plant Reconstruction by a Robot—0
SSL-OTA: Unveiling Backdoor Threats in Self-Supervised Learning for Object Detection—0
SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS—0
kNN Retrieval for Simple and Effective Zero-Shot Multi-speaker Text-to-Speech—0
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection—0
SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification—0
SSTFB: Leveraging self-supervised pretext learning and temporal self-attention with feature branching for real-time video polyp segmentation—0
SS-VAERR: Self-Supervised Apparent Emotional Reaction Recognition from Video—0
Stabilize, Decompose, and Denoise: Self-Supervised Fluoroscopy Denoising—0
Stain-Adaptive Self-Supervised Learning for Histopathology Image Analysis—0
State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?—0
Cross-modal Manifold Cutmix for Self-supervised Video Representation Learning—0
Stealthy Backdoor Attack in Self-Supervised Learning Vision Encoders for Large Vision Language Models—0
Stereo Matching by Self-supervision of Multiscopic Vision—0
STERLING: Synergistic Representation Learning on Bipartite Graphs—0
ST-KeyS: Self-Supervised Transformer for Keyword Spotting in Historical Handwritten Documents—0
STLight: a Fully Convolutional Approach for Efficient Predictive Learning by Spatio-Temporal joint Processing—0
Stochastic Contrastive Learning—0
Stochastic Vision Transformers with Wasserstein Distance-Aware Attention—0
Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning—0
ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting—0
Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation—0
Structured-Noise Masked Modeling for Video, Audio and Beyond—0
Strumming to the Beat: Audio-Conditioned Contrastive Video Textures—0
Stylebook: Content-Dependent Speaking Style Modeling for Any-to-Any Voice Conversion using Only Speech Data—0
StyleCap: Automatic Speaking-Style Captioning from Speech Based on Speech and Language Self-supervised Learning Models—0
Subgraph Networks Based Contrastive Learning—0
Subject-based Non-contrastive Self-Supervised Learning for ECG Signal Processing—0
Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Clutter—0
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities—0
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities—0
SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning—0
Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing—0
Supervised and self-supervised land-cover segmentation & classification of the Biesbosch wetlands—0
Supervised Contrastive Learning for Recommendation—0
Supervised Pretraining for Material Property Prediction—0
Supervision-Guided Codebooks for Masked Prediction in Speech Pre-training—0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution—0
SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning—0
SurgMAE: Masked Autoencoders for Long Surgical Video Analysis—0
Survey on Self-Supervised Multimodal Representation Learning and Foundation Models—0
Survey on Self-supervised Representation Learning Using Image Transformations—0
Sustainable self-supervised learning for speech representations—0
SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image—0
SwingBot: Learning Physical Features from In-hand Tactile Exploration for Dynamic Swing-up Manipulation—0
SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification—0
Symmetric masking strategy enhances the performance of Masked Image Modeling—0
SynDistNet: Self-Supervised Monocular Fisheye Camera Distance Estimation Synergized with Semantic Segmentation for Autonomous Driving—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