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 3001–3050 of 5044 papers

TitleStatusHype
Label-efficient Time Series Representation Learning: A Review—0
Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling—0
Label-free Monitoring of Self-Supervised Learning Progress—0
Label-free segmentation from cardiac ultrasound using self-supervised learning—0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
Ladder Siamese Network: a Method and Insights for Multi-level Self-Supervised Learning—0
LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space—0
Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection—0
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model—0
Language-based Action Concept Spaces Improve Video Self-Supervised Learning—0
Language Bias in Self-Supervised Learning For Automatic Speech Recognition—0
Language Models sounds the Death Knell of Knowledge Graphs—0
Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition—0
Laplacian Denoising Autoencoder—0
Large Cognition Model: Towards Pretrained EEG Foundation Model—0
Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents—0
Large-scale Foundation Models and Generative AI for BigData Neuroscience—0
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping—0
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
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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