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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3251–3300 of 5044 papers

TitleStatusHype
Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views—0
Contrastive Learning with Adversarial Examples—0
Contrastive Learning with Adaptive Neighborhoods for Brain Age Prediction on 3D Stiffness Maps—0
Contrastive Learning with Positive-Negative Frame Mask for Music Representation—0
Contrastive Left-Right Wearable Sensors (IMUs) Consistency Matching for HAR—0
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning—0
Contrastive Self-Supervised Learning As Neural Manifold Packing—0
Contrastive Self-supervised Learning for Graph Classification—0
Contrastive Self-Supervised Learning for Skeleton Representations—0
Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos—0
Contrastive Self-supervised Learning in Recommender Systems: A Survey—0
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations—0
Contrastive Separative Coding for Self-supervised Representation Learning—0
Controllable Face Manipulation and UV Map Generation by Self-supervised Learning—0
Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments—0
Conversational Query Rewriting with Self-supervised Learning—0
Convexity-based Pruning of Speech Representation Models—0
CooPre: Cooperative Pretraining for V2X Cooperative Perception—0
CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping—0
COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images—0
CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning—0
CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning—0
CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers—0
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning—0
Self-Supervised Tracking via Target-Aware Data Synthesis—0
Cross-BERT for Point Cloud Pretraining—0
Cross-Dimensional Medical Self-Supervised Representation Learning Based on a Pseudo-3D Transformation—0
Cross-domain few-shot learning with unlabelled data—0
Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning—0
Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels—0
Cross-domain Voice Activity Detection with Self-Supervised Representations—0
Cross-Entropy Is All You Need To Invert the Data Generating Process—0
CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond—0
Cross-Identity Motion Transfer for Arbitrary Objects through Pose-Attentive Video Reassembling—0
Cross-Level Multi-Instance Distillation for Self-Supervised Fine-Grained Visual Categorization—0
Cross-Modal Discrete Representation Learning—0
Cross-modal Image Retrieval with Deep Mutual Information Maximization—0
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space—0
Cross Pixel Optical Flow Similarity for Self-Supervised Learning—0
Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI—0
CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point Cloud Video Understanding—0
Cross-view and Cross-pose Completion for 3D Human Understanding—0
Cross-view Self-Supervised Learning on Heterogeneous Graph Neural Network via Bootstrapping—0
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction—0
CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languages—0
CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition—0
CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication—0
CUBE360: Learning Cubic Field Representation for Monocular 360 Depth Estimation for Virtual Reality—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