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 3051–3100 of 5044 papers

TitleStatusHype
Contrastive Learning for OOD in Object detectionCode0
Instance Image Retrieval by Learning Purely From Within the Dataset—0
On the Pros and Cons of Momentum Encoder in Self-Supervised Visual Representation Learning—0
Non-Contrastive Self-Supervised Learning of Utterance-Level Speech RepresentationsCode1
Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech—0
Consistency-based Self-supervised Learning for Temporal Anomaly LocalizationCode1
SIAD: Self-supervised Image Anomaly Detection System—0
SLiDE: Self-supervised LiDAR De-snowing through Reconstruction Difficulty—0
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation—0
Stain-Adaptive Self-Supervised Learning for Histopathology Image Analysis—0
AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning—0
Distributed Contrastive Learning for Medical Image Segmentation—0
Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social BehaviourCode0
SSDPT: Self-Supervised Dual-Path Transformer for Anomalous Sound Detection in Machine Condition Monitoring—0
Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning—0
TransDSSL: Transformer based Depth Estimation via Self-Supervised LearningCode1
Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions—0
Analyzing Data-Centric Properties for Graph Contrastive LearningCode0
Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection—0
RAZE: Region Guided Self-Supervised Gaze Representation Learning—0
Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 DiagnosisCode0
Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning—0
COCOA: Cross Modality Contrastive Learning for Sensor DataCode1
SdAE: Self-distillated Masked AutoencoderCode1
BYOLMed3D: Self-Supervised Representation Learning of Medical Videos using Gradient Accumulation Assisted 3D BYOL Framework—0
Improving Fine-tuning of Self-supervised Models with Contrastive InitializationCode0
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond—0
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
RCA: Ride Comfort-Aware Visual Navigation via Self-Supervised Learning—0
SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and SegmentationCode2
Self-supervised learning with rotation-invariant kernelsCode1
Self-Supervised Hypergraph Transformer for Recommender SystemsCode1
HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative—0
Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning—0
Time to augment self-supervised visual representation learning—0
Deep Clustering with Features from Self-Supervised Pretraining—0
Learning a Dual-Mode Speech Recognition Model via Self-Pruning—0
Dive into Big Model TrainingCode1
Dynamic Channel Selection in Self-Supervised LearningCode0
Explored An Effective Methodology for Fine-Grained Snake RecognitionCode0
Better Reasoning Behind Classification Predictions with BERT for Fake News Detection—0
Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosisCode1
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility—0
Adaptive Soft Contrastive LearningCode1
Scale dependant layer for self-supervised nuclei encodingCode0
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial RobustnessCode1
Hyper-Representations for Pre-Training and Transfer LearningCode1
Synthesizing Light Field Video from Monocular VideoCode1
KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view StereoCode1
MetaComp: Learning to Adapt for Online Depth Completion—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