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 1301–1350 of 5044 papers

TitleStatusHype
Self-Supervised Learning for Covariance Estimation—0
LAFS: Landmark-based Facial Self-supervised Learning for Face RecognitionCode1
Improving Acoustic Word Embeddings through Correspondence Training of Self-supervised Speech RepresentationsCode0
VANP: Learning Where to See for Navigation with Self-Supervised Vision-Action Pre-TrainingCode1
Verification-Aided Learning of Neural Network Barrier Functions with Termination GuaranteesCode0
AACP: Aesthetics assessment of children's paintings based on self-supervised learning—0
Intra-video Positive Pairs in Self-Supervised Learning for UltrasoundCode0
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models—0
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain—0
Out-of-distribution Partial Label Learning—0
Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge EnhancementCode2
SCORE: Self-supervised Correspondence Fine-tuning for Improved Content RepresentationsCode0
On depth prediction for autonomous driving using self-supervised learning—0
Can Generative Models Improve Self-Supervised Representation Learning?Code0
DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos—0
Learned 3D volumetric recovery of clouds and its uncertainty for climate analysis—0
Augmentations vs Algorithms: What Works in Self-Supervised Learning—0
SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target DetectionCode1
JointMotion: Joint Self-Supervision for Joint Motion Prediction—0
Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification—0
Self-Supervision in Time for Satellite Images(S3-TSS): A novel method of SSL technique in Satellite imagesCode0
Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology—0
Lightweight Cross-Modal Representation LearningCode0
On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors—0
Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition—0
Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift—0
Self-supervised Photographic Image Layout Representation LearningCode1
Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation—0
DINOv2 based Self Supervised Learning For Few Shot Medical Image SegmentationCode1
Pooling Image Datasets With Multiple Covariate Shift and Imbalance—0
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
FLGuard: Byzantine-Robust Federated Learning via Ensemble of Contrastive ModelsCode0
A Theoretical Analysis of Self-Supervised Learning for Vision Transformers—0
HeAR -- Health Acoustic Representations—0
Perceptive self-supervised learning network for noisy image watermark removalCode1
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques—0
Self-Supervised Representation Learning with Meta Comprehensive Regularization—0
Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTVCode2
Dynamic 3D Point Cloud Sequences as 2D VideosCode2
Leveraging Self-Supervised Learning for Scene Classification in Child Sexual Abuse Imagery—0
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling—0
Rethinking The Uniformity Metric in Self-Supervised LearningCode0
Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised LearningCode1
MENTOR: Multi-level Self-supervised Learning for Multimodal RecommendationCode1
VideoMAC: Video Masked Autoencoders Meet ConvNetsCode1
Automated segmentation of lesions and organs at risk on [68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETRCode1
Atmospheric Turbulence Removal with Video Sequence Deep Visual Priors—0
Compact Speech Translation Models via Discrete Speech Units Pretraining—0
Towards Better Understanding of Contrastive Sentence Representation Learning: A Unified Paradigm for GradientCode0
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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