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 951–1000 of 5044 papers

TitleStatusHype
SelfKG: Self-Supervised Entity Alignment in Knowledge GraphsCode1
Self-Supervised Scene Flow Estimation with 4-D Automotive RadarCode1
Domain Knowledge-Informed Self-Supervised Representations for Workout Form AssessmentCode1
Reconstruction of Perceived Images from fMRI Patterns and Semantic Brain Exploration using Instance-Conditioned GANsCode1
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech RecognitionCode1
Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentationCode1
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm PerformanceCode1
S3T: Self-Supervised Pre-training with Swin Transformer for Music ClassificationCode1
Towards better understanding and better generalization of few-shot classification in histology images with contrastive learningCode1
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment ContrastCode1
Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-TrainingCode1
Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised trainingCode1
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?Code1
Energy-Based Contrastive Learning of Visual RepresentationsCode1
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RLCode1
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
Graph Self-supervised Learning with Accurate Discrepancy LearningCode1
Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech RecognitionCode1
Backdoor Defense via Decoupling the Training ProcessCode1
Intent Contrastive Learning for Sequential RecommendationCode1
Self-supervised Learning with Random-projection Quantizer for Speech RecognitionCode1
Adversarial Masking for Self-Supervised LearningCode1
Graph Representation Learning via Aggregation EnhancementCode1
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform LossCode1
SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained EncodersCode1
SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature VerificationCode1
Neural Manifold Clustering and EmbeddingCode1
Revisiting Weakly Supervised Pre-Training of Visual Perception ModelsCode1
Self-Supervised Deep Blind Video Super-ResolutionCode1
Time Series Generation with Masked AutoencoderCode1
Boundary-aware Self-supervised Learning for Video Scene SegmentationCode1
Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motionCode1
Progressive Video Summarization via Multimodal Self-supervised LearningCode1
MGAE: Masked Autoencoders for Self-Supervised Learning on GraphsCode1
Implicit Autoencoder for Point-Cloud Self-Supervised Representation LearningCode1
Optimal Representations for Covariate ShiftCode1
Continually Learning Self-Supervised Representations with Projected Functional RegularizationCode1
Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth EstimationCode1
SLIP: Self-supervision meets Language-Image Pre-trainingCode1
UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality BarrierCode1
Self-Supervised Learning for speech recognition with Intermediate layer supervisionCode1
High Fidelity Visualization of What Your Self-Supervised Representation Knows AboutCode1
Self-Supervised Monocular Depth and Ego-Motion Estimation in Endoscopy: Appearance Flow to the RescueCode1
On the use of Cortical Magnification and Saccades as Biological Proxies for Data AugmentationCode1
Learning Representations with Contrastive Self-Supervised Learning for Histopathology ApplicationsCode1
Exploring the Equivalence of Siamese Self-Supervised Learning via A Unified Gradient FrameworkCode1
TCGL: Temporal Contrastive Graph for Self-supervised Video Representation LearningCode1
BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net modelsCode1
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image FusionCode1
Augmentation-Free Self-Supervised Learning on GraphsCode1
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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