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 2401–2450 of 5044 papers

TitleStatusHype
Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading—0
FedMAE: Federated Self-Supervised Learning with One-Block Masked Auto-Encoder—0
Knowledge Distillation from Multiple Foundation Models for End-to-End Speech Recognition—0
Coreset Sampling from Open-Set for Fine-Grained Self-Supervised LearningCode1
A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition—0
Cocktail HuBERT: Generalized Self-Supervised Pre-training for Mixture and Single-Source Speech—0
A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide ImagesCode0
Self-Supervised Learning for Multimodal Non-Rigid 3D Shape MatchingCode1
HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image SegmentationCode1
Exploring Expression-related Self-supervised Learning for Affective Behaviour AnalysisCode0
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
Contrastive Self-supervised Learning in Recommender Systems: A Survey—0
Robust Semi-Supervised Learning for Histopathology Images through Self-Supervision Guided Out-of-Distribution Scoring—0
Unified Mask Embedding and Correspondence Learning for Self-Supervised Video SegmentationCode0
Toward Super-Resolution for Appearance-Based Gaze Estimation—0
Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-MpoxCode0
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view ClusteringCode1
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition—0
All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy ReductionCode0
Unsupervised Facial Expression Representation Learning with Contrastive Local WarpingCode0
SSL-Cleanse: Trojan Detection and Mitigation in Self-Supervised LearningCode0
Self-Supervised Visual Representation Learning on Food Images—0
RGI : Regularized Graph Infomax for self-supervised learning on graphs—0
Learning to Reconstruct Signals From Binary MeasurementsCode0
Fully neuromorphic vision and control for autonomous drone flight—0
Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image ClassificationCode1
Learning Cross-lingual Visual Speech Representations—0
Improving Accented Speech Recognition with Multi-Domain Training—0
Automated Self-Supervised Learning for RecommendationCode2
A Hierarchical Regression Chain Framework for Affective Vocal Burst RecognitionCode0
OVRL-V2: A simple state-of-art baseline for ImageNav and ObjectNav—0
Sample-efficient Adversarial Imitation Learning—0
Lightweight feature encoder for wake-up word detection based on self-supervised speech representation—0
Efficient Self-supervised Continual Learning with Progressive Task-correlated Layer Freezing—0
Three Guidelines You Should Know for Universally Slimmable Self-Supervised LearningCode1
FireRisk: A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised LearningCode1
Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identificationCode1
Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection—0
Synthetic Experience ReplayCode1
Functional Knowledge Transfer with Self-supervised Representation LearningCode0
Improving Masked Autoencoders by Learning Where to Mask—0
Fine-tuning Strategies for Faster Inference using Speech Self-Supervised Models: A Comparative StudyCode0
Extending global-local view alignment for self-supervised learning with remote sensing imageryCode1
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast—0
PRSNet: A Masked Self-Supervised Learning Pedestrian Re-Identification MethodCode0
Stabilizing Transformer Training by Preventing Attention Entropy CollapseCode2
Towards domain-invariant Self-Supervised Learning with Batch Styles Standardization—0
UNFUSED: UNsupervised Finetuning Using SElf supervised DistillationCode1
Self-supervised Facial Action Unit Detection with Region and Relation Learning—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