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 4001–4050 of 5044 papers

TitleStatusHype
Large-scale Training of Foundation Models for Wearable Biosignals—0
Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning—0
Latent Programmer: Discrete Latent Codes for Program Synthesis—0
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression—0
LAVIS: A Library for Language-Vision Intelligence—0
Layered Depth Refinement with Mask Guidance—0
Layer-wise Investigation of Large-Scale Self-Supervised Music Representation Models—0
LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity—0
Learnability and Expressiveness in Self-Supervised Learning—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation—0
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning—0
Learned 3D volumetric recovery of clouds and its uncertainty for climate analysis—0
Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstruction—0
Learning 3D Face Reconstruction with a Pose Guidance Network—0
Learning 3D Representations from Procedural 3D Programs—0
Learning a Dual-Mode Speech Recognition Model via Self-Pruning—0
Learning audio representations via phase prediction—0
Learning Background Invariance Improves Generalization and Robustness in Self-Supervised Learning on ImageNet and Beyond—0
Learning based convex approximation for constrained parametric optimization—0
Learning Beyond Similarities: Incorporating Dissimilarities between Positive Pairs in Self-Supervised Time Series Learning—0
Learning by Aligning 2D Skeleton Sequences and Multi-Modality Fusion—0
Learning by Distillation: A Self-Supervised Learning Framework for Optical Flow Estimation—0
Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles—0
Learning Complete 3D Morphable Face Models from Images and Videos—0
Learning Compound Tasks without Task-specific Knowledge via Imitation and Self-supervised Learning—0
Learning Contextually Fused Audio-visual Representations for Audio-visual Speech Recognition—0
Learning Cross-lingual Visual Speech Representations—0
Learning Cross-view Visual Geo-localization without Ground Truth—0
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering—0
Learning Dense Reward with Temporal Variant Self-Supervision—0
Learning Dependencies of Discrete Speech Representations with Neural Hidden Markov Models—0
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
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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