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
DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean dataCode1
BenchMD: A Benchmark for Unified Learning on Medical Images and SensorsCode1
Multi-Mode Online Knowledge Distillation for Self-Supervised Visual Representation LearningCode1
Micron-BERT: BERT-based Facial Micro-Expression RecognitionCode1
Self-Supervised Video Similarity LearningCode1
Defending Against Patch-based Backdoor Attacks on Self-Supervised LearningCode1
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue DistributionCode1
Dynamic Conceptional Contrastive Learning for Generalized Category DiscoveryCode1
Kaizen: Practical Self-supervised Continual Learning with Continual Fine-tuningCode1
Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural CircuitsCode1
Point2Vec for Self-Supervised Representation Learning on Point CloudsCode1
Spatiotemporal Self-supervised Learning for Point Clouds in the WildCode1
Spatially Adaptive Self-Supervised Learning for Real-World Image DenoisingCode1
Contrastive Learning Is Spectral Clustering On Similarity GraphCode1
Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete LabelsCode1
Detecting Backdoors in Pre-trained EncodersCode1
A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action RecognitionCode1
Temperature Schedules for Self-Supervised Contrastive Methods on Long-Tail DataCode1
Self-Supervised Learning for Multimodal Non-Rigid 3D Shape MatchingCode1
Coreset Sampling from Open-Set for Fine-Grained Self-Supervised LearningCode1
HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image SegmentationCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view ClusteringCode1
Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image ClassificationCode1
Three Guidelines You Should Know for Universally Slimmable Self-Supervised LearningCode1
Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identificationCode1
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised LearningCode1
Extending global-local view alignment for self-supervised learning with remote sensing imageryCode1
Synthetic Experience ReplayCode1
UNFUSED: UNsupervised Finetuning Using SElf supervised DistillationCode1
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity RepresentationsCode1
A Self-Correcting Sequential RecommenderCode1
Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition ProblemsCode1
Contrastive Hierarchical ClusteringCode1
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive LearningCode1
Rethinking the Effect of Data Augmentation in Adversarial Contrastive LearningCode1
ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud CompletionCode1
Heterogeneous Graph Contrastive Learning for RecommendationCode1
Mosaic Representation Learning for Self-supervised Visual Pre-trainingCode1
Internet Explorer: Targeted Representation Learning on the Open WebCode1
Improving Adaptive Conformal Prediction Using Self-Supervised LearningCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the LeastCode1
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton SequencesCode1
Self-supervised learning of Split Invariant Equivariant representationsCode1
Multi-Source Contrastive Learning from Musical AudioCode1
Semiconductor Fab Scheduling with Self-Supervised and Reinforcement LearningCode1
Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image AnalysisCode1
Evaluating Self-Supervised Learning via Risk DecompositionCode1
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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