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 601–650 of 5044 papers

TitleStatusHype
Chasing Clouds: Differentiable Volumetric Rasterisation of Point Clouds as a Highly Efficient and Accurate Loss for Large-Scale Deformable 3D RegistrationCode1
Boundary-aware Self-supervised Learning for Video Scene SegmentationCode1
Digging Into Self-Supervised Monocular Depth EstimationCode1
Digging into Uncertainty in Self-supervised Multi-view StereoCode1
DINOv2 based Self Supervised Learning For Few Shot Medical Image SegmentationCode1
DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image AnalysisCode1
Anomaly Detection Requires Better RepresentationsCode1
Decoupling Common and Unique Representations for Multimodal Self-supervised LearningCode1
An Unsupervised Sentence Embedding Method by Mutual Information MaximizationCode1
Distance-wise Prototypical Graph Neural Network in Node Imbalance ClassificationCode1
3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal PerspectiveCode1
ChemBERTa-2: Towards Chemical Foundation ModelsCode1
Dive into Big Model TrainingCode1
Dive into Self-Supervised Learning for Medical Image Analysis: Data, Models and TasksCode1
ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud CompletionCode1
Civil Rephrases Of Toxic Texts With Self-Supervised TransformersCode1
DOBF: A Deobfuscation Pre-Training Objective for Programming LanguagesCode1
DocMAE: Document Image Rectification via Self-supervised Representation LearningCode1
Broaden Your Views for Self-Supervised Video LearningCode1
Broken Neural Scaling LawsCode1
CL4CTR: A Contrastive Learning Framework for CTR PredictionCode1
Domain Knowledge-Informed Self-Supervised Representations for Workout Form AssessmentCode1
Boosting Contrastive Self-Supervised Learning with False Negative CancellationCode1
DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge ComputingCode1
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical imagesCode1
Dual Intents Graph Modeling for User-centric Group DiscoveryCode1
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised LearningCode1
Adopting Self-Supervised Learning into Unsupervised Video Summarization through Restorative Score.Code1
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio RepresentationCode1
Deeper into Self-Supervised Monocular Indoor Depth EstimationCode1
BYOL-S: Learning Self-supervised Speech Representations by BootstrappingCode1
Defending Against Patch-based Backdoor Attacks on Self-Supervised LearningCode1
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language ModelingCode1
CAiD: Context-Aware Instance Discrimination for Self-supervised Learning in Medical ImagingCode1
Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object DetectionCode1
A Random CNN Sees Objects: One Inductive Bias of CNN and Its ApplicationsCode1
Dissecting Self-Supervised Learning Methods for Surgical Computer VisionCode1
Efficient and Information-Preserving Future Frame Prediction and BeyondCode1
Data-Efficient Reinforcement Learning with Self-Predictive RepresentationsCode1
Anomaly Detection in Video via Self-Supervised and Multi-Task LearningCode1
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical ImagingCode1
Efficient Self-supervised Vision Pretraining with Local Masked ReconstructionCode1
Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural FieldsCode1
Adversarial Examples Are Not Real FeaturesCode1
A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-SupervisionCode1
EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation LearningCode1
Adopting Self-Supervised Learning into Unsupervised Video Summarization through Restorative ScoreCode1
Blockwise Self-Supervised Learning at ScaleCode1
CCGL: Contrastive Cascade Graph LearningCode1
Deciphering and integrating invariants for neural operator learning with various physical mechanismsCode1
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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