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 2251–2300 of 5044 papers

TitleStatusHype
DECAR: Deep Clustering for learning general-purpose Audio RepresentationsCode0
Towards Adversarial Robustness And Backdoor Mitigation in SSLCode0
Benchmark for Uncertainty & Robustness in Self-Supervised LearningCode0
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential RecommendationCode0
Learning to Reconstruct Signals From Binary MeasurementsCode0
Learning Useful Representations of Recurrent Neural Network Weight MatricesCode0
Investigate the Essence of Long-Tailed Recognition from a Unified PerspectiveCode0
Learning to Plan for Language Modeling from Unlabeled DataCode0
Learning to Exploit Temporal Structure for Biomedical Vision-Language ProcessingCode0
An efficient framework based on large foundation model for cervical cytopathology whole slide image screeningCode0
PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate DistributionsCode0
Learning to Edit Visual Programs with Self-SupervisionCode0
Leveraging Visual Supervision for Array-based Active Speaker Detection and LocalizationCode0
Masked Image Modeling as a Framework for Self-Supervised Learning across Eye MovementsCode0
ACE: Anatomically Consistent Embeddings in Composition and DecompositionCode0
Mixtures of Experts Unlock Parameter Scaling for Deep RLCode0
Quantitative Imaging Principles Improves Medical Image LearningCode0
Deep Active Learning Using Barlow Twins—0
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals—0
Deep Active Ensemble Sampling For Image Classification—0
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning—0
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems—0
Interventional Contrastive Learning with Meta Semantic Regularizer—0
DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders—0
Bayesian Graph Contrastive Learning—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction—0
Combining Self-Supervised and Supervised Learning with Noisy Labels—0
Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data—0
Interpretable agent communication from scratch (with a generic visual processor emerging on the side)—0
Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation—0
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph—0
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution—0
Adaptive Multi-layer Contrastive Graph Neural Networks—0
Intermediate Self-supervised Learning for Machine Translation Quality Estimation—0
Decoupled Self-supervised Learning for Non-Homophilous Graphs—0
Interest-oriented Universal User Representation via Contrastive Learning—0
Interactive Masked Image Modeling for Multimodal Object Detection in Remote Sensing—0
Interactive Feature Embedding for Infrared and Visible Image Fusion—0
Intent Disentanglement and Feature Self-supervision for Novel Recommendation—0
AND: Audio Network Dissection for Interpreting Deep Acoustic Models—0
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering—0
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation—0
Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification—0
Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition—0
Integrating Emotional and Linguistic Models for Ethical Compliance in Large Language Models—0
Integrating Auxiliary Information in Self-supervised Learning—0
Deciphering the Projection Head: Representation Evaluation Self-supervised Learning—0
BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learning—0
An Autoencoder-based Snow Drought Index—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