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 3051–3100 of 5044 papers

TitleStatusHype
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees—0
Learning Downstream Task by Selectively Capturing Complementary Knowledge from Multiple Self-supervisedly Learning Pretexts—0
A Simple Self-Supervised ECG Representation Learning Method via Manipulated Temporal-Spatial Reverse Detection—0
Learning event representations for temporal segmentation of image sequences by dynamic graph embedding—0
Learning Fashion Compatibility from In-the-wild Images—0
Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks—0
Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI—0
Learning From Long-Tailed Data With Noisy Labels—0
Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis—0
Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery—0
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency—0
Learning How To Ask: Cycle-Consistency Refines Prompts in Multimodal Foundation Models—0
Learning imaging mechanism directly from optical microscopy observations—0
Learning Compact and Robust Representations for Anomaly Detection—0
Learning Invariant World State Representations with Predictive Coding—0
Self-supervised Learning of Interpretable Keypoints from Unlabelled Videos—0
Learning Library Cell Representations in Vector Space—0
Learning Low-Rank Feature for Thorax Disease Classification—0
Learning Mask Invariant Mutual Information for Masked Image Modeling—0
Learning Minimal Representations with Model Invariance—0
Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling—0
Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation—0
Learning neural audio features without supervision—0
Learning Object-Centric Video Models by Contrasting Sets—0
Learning Object Focused Attention—0
Learning Off-Road Terrain Traversability with Self-Supervisions Only—0
Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation—0
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels—0
Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry—0
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation—0
LEARNING PHONEME-LEVEL DISCRETE SPEECH REPRESENTATION WITH WORD-LEVEL SUPERVISION—0
Learning Pixel Trajectories with Multiscale Contrastive Random Walks—0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
Learning Rotation-Equivariant Features for Visual Correspondence—0
Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels—0
Learning Spatial-Semantic Relationship for Facial Attribute Recognition With Limited Labeled Data—0
Learning Speech Representations from Raw Audio by Joint Audiovisual Self-Supervision—0
Learning Symbolic Representations Through Joint GEnerative and DIscriminative Training—0
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture—0
Learning Task-Independent Game State Representations from Unlabeled Images—0
Learning to Customize Text-to-Image Diffusion In Diverse Context—0
Learning to Discover Reflection Symmetry via Polar Matching Convolution—0
Learning to Efficiently Adapt Foundation Models for Self-Supervised Endoscopic 3D Scene Reconstruction from Any Cameras—0
Learning To Explore With Predictive World Model Via Self-Supervised Learning—0
Learning to Generalize One Sample at a Time with Self-Supervision—0
Learning to Identify Physical Parameters from Video Using Differentiable Physics—0
Socially Supervised Representation Learning: the Role of Subjectivity in Learning Efficient Representations—0
Learning to Learn in a Semi-Supervised Fashion—0
Learning to Model the World with Language—0
Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control—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