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 1401–1450 of 5044 papers

TitleStatusHype
Evaluating Self-Supervised Learning for Molecular Graph EmbeddingsCode1
Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue DetectionCode1
Evaluating Self-Supervised Learning via Risk DecompositionCode1
SelfAugment: Automatic Augmentation Policies for Self-Supervised LearningCode1
Civil Rephrases Of Toxic Texts With Self-Supervised TransformersCode1
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural RepresentationCode1
CL4CTR: A Contrastive Learning Framework for CTR PredictionCode1
Self-Supervised Bug Detection and RepairCode1
A Self-supervised Method for Entity AlignmentCode1
SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial DefectsCode1
Overcoming Language Priors with Self-supervised Learning for Visual Question AnsweringCode1
Self-supervised contrastive learning performs non-linear system identificationCode1
Evaluation of Speech Representations for MOS predictionCode1
A Fast Knowledge Distillation Framework for Visual RecognitionCode1
3D Self-Supervised Methods for Medical ImagingCode1
Self-supervised driven consistency training for annotation efficient histopathology image analysisCode1
Self-Supervised Ego-Motion Estimation Based on Multi-Layer Fusion of RGB and Inferred DepthCode1
CoLES: Contrastive Learning for Event Sequences with Self-SupervisionCode1
Every Node is Different: Dynamically Fusing Self-Supervised Tasks for Attributed Graph ClusteringCode1
Evidence of Vocal Tract Articulation in Self-Supervised Learning of SpeechCode1
Self-Supervised Few-Shot Learning on Point CloudsCode1
Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant ClusteringCode1
A comprehensive survey on deep active learning in medical image analysisCode1
From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image SegmentationCode1
EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based VisionCode1
Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event PredictionCode1
Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchangeCode1
Self-supervised Re-renderable Facial Albedo Reconstruction from Single ImageCode1
Self-Supervised Hypergraph Convolutional Networks for Session-based RecommendationCode1
Self-Supervised Hypergraph Transformer for Recommender SystemsCode1
Self-Supervised Learning by Estimating Twin Class DistributionsCode1
Self-Supervised Learning Disentangled Group Representation as FeatureCode1
Self-Supervised Learning for Anomalous Sound DetectionCode1
Unsupervised Representation Learning for Binary Networks by Joint Classifier LearningCode1
A Simple and Efficient Baseline for Data Attribution on ImagesCode1
Self-supervised Learning for ECG-based Emotion RecognitionCode1
Self-Supervised Multi-Object Tracking with Cross-Input ConsistencyCode1
Deep Active Learning Using Barlow Twins—0
Deep Active Ensemble Sampling For Image Classification—0
Bayesian Graph Contrastive Learning—0
DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders—0
Adaptive Multi-layer Contrastive Graph Neural Networks—0
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution—0
Combining Self-Supervised and Supervised Learning with Noisy Labels—0
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph—0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning—0
GraphCL: Contrastive Self-Supervised Learning of Graph Representations—0
Decoupled Self-supervised Learning for Non-Homophilous Graphs—0
AND: Audio Network Dissection for Interpreting Deep Acoustic Models—0
Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification—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