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 4001–4050 of 5044 papers

TitleStatusHype
Self-Supervised Graph Co-Training for Session-based RecommendationCode1
Graph Neural Networks: Methods, Applications, and Opportunities—0
Jointly Learnable Data Augmentations for Self-Supervised GNNsCode1
Generative and Contrastive Self-Supervised Learning for Graph Anomaly DetectionCode1
Semi-supervised learning for joint SAR and multispectral land cover classification—0
Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue DetectionCode1
Topo2vec: Topography Embedding Using the Fractal EffectCode0
Self-Supervised Video Representation Learning with Meta-Contrastive Network—0
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning—0
Self-Supervised Visual Representations Learning by Contrastive Mask Prediction—0
Self-Supervised 3D Human Pose Estimation with Multiple-View GeometryCode0
MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3D CT Lesions—0
Investigating a Baseline Of Self Supervised Learning Towards Reducing Labeling Costs For Image Classification—0
RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks—0
Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping—0
Improving Self-supervised Learning with Hardness-aware Dynamic Curriculum Learning: An Application to Digital PathologyCode1
Focus on the Positives: Self-Supervised Learning for Biodiversity MonitoringCode0
Contrastive Self-supervised Sequential Recommendation with Robust AugmentationCode1
Collaborative Unsupervised Visual Representation Learning from Decentralized DataCode0
Is Pseudo-Lidar needed for Monocular 3D Object detection?Code1
Dual Path Learning for Domain Adaptation of Semantic SegmentationCode1
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language ProcessingCode1
Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion ApproachCode1
Cervical Optical Coherence Tomography Image Classification Based on Contrastive Self-Supervised Texture LearningCode0
Self-supervised Consensus Representation Learning for Attributed GraphCode0
How Self-Supervised Learning Can be Used for Fine-Grained Head Pose Estimation?—0
Self-supervised Learning of Occlusion Aware Flow Guided 3D Geometry Perception with Adaptive Cross Weighted Loss from Monocular Videos—0
Skeleton-Contrastive 3D Action Representation LearningCode1
Improving Contrastive Learning by Visualizing Feature TransformationCode1
Co-learning: Learning from Noisy Labels with Self-supervisionCode1
Self-Supervised Learning from Unlabeled Fundus Photographs Improves Segmentation of the Retina—0
Self-Supervised Learning of Depth and Ego-Motion from Video by Alternative Training and Geometric Constraints from 3D to 2D—0
Solo-learn: A Library of Self-supervised Methods for Visual Representation LearningCode0
Self-supervised Learning with Local Attention-Aware Feature—0
BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised LearningCode1
Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction—0
Object-aware Contrastive Learning for Debiased Scene RepresentationCode1
Self-Supervised Learning for Fine-Grained Image ClassificationCode1
Experimenting with Self-Supervision using Rotation Prediction for Image CaptioningCode0
CCGL: Contrastive Cascade Graph LearningCode1
Discriminative-Generative Representation Learning for One-Class Anomaly Detection—0
DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose EstimationCode1
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning—0
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning—0
AASAE: Augmentation-Augmented Stochastic AutoencodersCode1
Hand Image Understanding via Deep Multi-Task LearningCode1
Trip-ROMA: Self-Supervised Learning with Triplets and Random MappingsCode0
On the Memorization Properties of Contrastive Learning—0
ReSSL: Relational Self-Supervised Learning with Weak AugmentationCode1
Group Contrastive Self-Supervised Learning on Graphs—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