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 1801–1850 of 5044 papers

TitleStatusHype
Learning Compact and Robust Representations for Anomaly Detection—0
A Unified Framework for Foreground and Anonymization Area Segmentation in CT and MRI DataCode0
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images—0
Edit as You See: Image-guided Video Editing via Masked Motion Modeling—0
SELMA3D challenge: Self-supervised learning for 3D light-sheet microscopy image segmentation—0
TrojanDec: Data-free Detection of Trojan Inputs in Self-supervised Learning—0
Radar Signal Recognition through Self-Supervised Learning and Domain Adaptation—0
Information-Maximized Soft Variable Discretization for Self-Supervised Image Representation LearningCode0
Towards a Generalizable Speech Marker for Parkinson's Disease Diagnosis—0
An Empirical Study of Accuracy-Robustness Tradeoff and Training Efficiency in Self-Supervised LearningCode0
Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions—0
LOHA: Direct Graph Spectral Contrastive Learning Between Low-pass and High-pass Views—0
Human Gaze Boosts Object-Centered Representation Learning—0
Gaussian Masked Autoencoders—0
Noise-Robust Target-Speaker Voice Activity Detection Through Self-Supervised Pretraining—0
Seeing the Whole in the Parts in Self-Supervised Representation Learning—0
Enhancing Contrastive Learning for Retinal Imaging via Adjusted Augmentation Scales—0
Representation Learning of Lab Values via Masked AutoEncoderCode0
Self-Supervised Learning for Detecting AI-Generated Faces as AnomaliesCode0
3D Cloud reconstruction through geospatially-aware Masked Autoencoders—0
AdaCrossNet: Adaptive Dynamic Loss Weighting for Cross-Modal Contrastive Point Cloud LearningCode0
HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment—0
Adapting Pre-trained 3D Models for Point Cloud Video Understanding via Cross-frame Spatio-temporal Perception—0
ABBSPO: Adaptive Bounding Box Scaling and Symmetric Prior based Orientation Prediction for Detecting Aerial Image Objects—0
Self-Supervised Learning for Color Spike Camera ReconstructionCode0
Spectral Informed Mamba for Robust Point Cloud Processing—0
UCM-VeID V2: A Richer Dataset and A Pre-training Method for UAV Cross-Modality Vehicle Re-Identification—0
Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature SpaceCode0
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution—0
VLMs-Guided Representation Distillation for Efficient Vision-Based Reinforcement Learning—0
TAGA: Self-supervised Learning for Template-free Animatable Gaussian Articulated Model—0
RAEncoder: A Label-Free Reversible Adversarial Examples Encoder for Dataset Intellectual Property Protection—0
Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis—0
Multi-modal Vision Pre-training for Medical Image Analysis—0
BOE-ViT: Boosting Orientation Estimation with Equivariance in Self-Supervised 3D Subtomogram Alignment—0
Metadata-Enhanced Speech Emotion Recognition: Augmented Residual Integration and Co-Attention in Two-Stage Fine-Tuning—0
NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics—0
EmoReg: Directional Latent Vector Modeling for Emotional Intensity Regularization in Diffusion-based Voice Conversion—0
Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models—0
Towards Better Spherical Sliced-Wasserstein Distance Learning with Data-Adaptive Discriminative Projection Direction—0
Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for Robustness, Generalizability, and Multi-Domain Impact—0
Causal Speech Enhancement with Predicting Semantics based on Quantized Self-supervised Learning Features—0
Automatic Self-supervised Learning for Social Recommendations—0
AV-DTEC: Self-Supervised Audio-Visual Fusion for Drone Trajectory Estimation and ClassificationCode0
Where Did Your Model Learn That? Label-free Influence for Self-supervised LearningCode0
BloomCoreset: Fast Coreset Sampling using Bloom Filters for Fine-Grained Self-Supervised LearningCode0
Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks—0
LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot LearningCode0
Self-supervised Spatial-Temporal Learner for Precipitation NowcastingCode0
EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation—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