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 2151–2200 of 5044 papers

TitleStatusHype
Demographic Predictability in 3D CT Foundation EmbeddingsCode0
Manifold Characteristics That Predict Downstream Task PerformanceCode0
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systemsCode0
Manifold Contrastive Learning with Variational Lie Group OperatorsCode0
MAGMA: Manifold Regularization for MAEsCode0
Lung Nodule-SSM: Self-Supervised Lung Nodule Detection and Classification in Thoracic CT ImagesCode0
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature ForgettingCode0
Magnitude-Phase Dual-Path Speech Enhancement Network based on Self-Supervised Embedding and Perceptual Contrast Stretch BoostingCode0
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasksCode0
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D CavitiesCode0
Degradation Self-Supervised Learning for Lithium-ion Battery Health DiagnosticsCode0
DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature NoiseCode0
BEST-STD: Bidirectional Mamba-Enhanced Speech Tokenization for Spoken Term DetectionCode0
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement LearningCode0
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised LearningCode0
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervisionCode0
Look Ahead or Look Around? A Theoretical Comparison Between Autoregressive and Masked PretrainingCode0
KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge BasesCode0
Looking Beyond Corners: Contrastive Learning of Visual Representations for Keypoint Detection and Description ExtractionCode0
Patch-Wise Self-Supervised Visual Representation Learning: A Fine-Grained ApproachCode0
Local Masking Meets Progressive Freezing: Crafting Efficient Vision Transformers for Self-Supervised LearningCode0
JOSENet: A Joint Stream Embedding Network for Violence Detection in Surveillance VideosCode0
Large-Scale Hyperspectral Image Clustering Using Contrastive LearningCode0
Large-scale pretraining on pathological images for fine-tuning of small pathological benchmarksCode0
Deep Unsupervised Learning for 3D ALS Point Cloud Change DetectionCode0
Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human ParsingCode0
Low-Rank Approximation of Structural Redundancy for Self-Supervised LearningCode0
MAP: A Model-agnostic Pretraining Framework for Click-through Rate PredictionCode0
Measuring the Robustness of Audio Deepfake DetectorsCode0
Joint-task Self-supervised Learning for Temporal CorrespondenceCode0
Link Prediction with Non-Contrastive LearningCode0
Linear-Complexity Self-Supervised Learning for Speech ProcessingCode0
An Empirical Study Of Self-supervised Learning Approaches For Object Detection With TransformersCode0
Detecting Side Effects of Adverse Drug Reactions Through Drug-Drug Interactions Using Graph Neural Networks and Self-Supervised LearningCode0
LiPCoT: Linear Predictive Coding based Tokenizer for Self-supervised Learning of Time Series Data via Language ModelsCode0
LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption GenerationCode0
Deep Spectral Improvement for Unsupervised Image Instance SegmentationCode0
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancerCode0
Benchmarking Self-Supervised Learning Methods for Accelerated MRI ReconstructionCode0
Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant PhenotypingCode0
Leveraging Visual Supervision for Array-based Active Speaker Detection and LocalizationCode0
Leveraging Pre-Trained Acoustic Feature Extractor For Affective Vocal Bursts TasksCode0
Deep self-supervised learning with visualisation for automatic gesture recognitionCode0
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly DetectionCode0
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN ArchitectureCode0
Liver Fibrosis and NAS scoring from CT images using self-supervised learning and texture encodingCode0
Deep Reinforcement Learning for Synthesizing Functions in Higher-Order LogicCode0
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream TasksCode0
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the WildCode0
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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