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 3251–3300 of 5044 papers

TitleStatusHype
Incorporating Unlabelled Data into Bayesian Neural Networks—0
Pac-HuBERT: Self-Supervised Music Source Separation via Primitive Auditory Clustering and Hidden-Unit BERT—0
Multi-Modal Representation Learning with Text-Driven Soft Masks—0
Self-Supervised learning for Neural Architecture Search (NAS)—0
Self-Supervised Learning-Based Source Separation for Meeting Data—0
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature ForgettingCode0
Mask Hierarchical Features For Self-Supervised Learning—0
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of ActionsCode0
Automatic Detection of Out-of-body Frames in Surgical Videos for Privacy Protection Using Self-supervised Learning and Minimal Labels—0
INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields—0
ISSTAD: Incremental Self-Supervised Learning Based on Transformer for Anomaly Detection and LocalizationCode0
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning—0
Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition—0
De-coupling and De-positioning Dense Self-supervised LearningCode0
Are Data-driven Explanations Robust against Out-of-distribution Data?Code0
Data Efficient Contrastive Learning in Histopathology using Active SamplingCode0
Large-scale pretraining on pathological images for fine-tuning of small pathological benchmarksCode0
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need—0
On the Stepwise Nature of Self-Supervised LearningCode0
Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning—0
Federated Learning without Full Labels: A Survey—0
Learning Rotation-Equivariant Features for Visual Correspondence—0
Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space—0
PointGame: Geometrically and Adaptively Masked Auto-Encoder on Point Clouds—0
Self-supervised Learning with Speech Modulation Dropout—0
Frozen Language Model Helps ECG Zero-Shot Learning—0
Multi-view Feature Extraction based on Triple Contrastive Heads—0
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillationCode0
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
Cocktail HuBERT: Generalized Self-Supervised Pre-training for Mixture and Single-Source Speech—0
Knowledge Distillation from Multiple Foundation Models for End-to-End Speech Recognition—0
A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition—0
A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide ImagesCode0
Exploring Expression-related Self-supervised Learning for Affective Behaviour AnalysisCode0
Unified Mask Embedding and Correspondence Learning for Self-Supervised Video SegmentationCode0
Toward Super-Resolution for Appearance-Based Gaze Estimation—0
Contrastive Self-supervised Learning in Recommender Systems: A Survey—0
Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-MpoxCode0
Robust Semi-Supervised Learning for Histopathology Images through Self-Supervision Guided Out-of-Distribution Scoring—0
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition—0
Unsupervised Facial Expression Representation Learning with Contrastive Local WarpingCode0
SSL-Cleanse: Trojan Detection and Mitigation in Self-Supervised LearningCode0
All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy ReductionCode0
Self-Supervised Visual Representation Learning on Food Images—0
RGI : Regularized Graph Infomax for self-supervised learning on graphs—0
Fully neuromorphic vision and control for autonomous drone flight—0
Learning to Reconstruct Signals From Binary MeasurementsCode0
Improving Accented Speech Recognition with Multi-Domain Training—0
OVRL-V2: A simple state-of-art baseline for ImageNav and ObjectNav—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