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 351–400 of 5044 papers

TitleStatusHype
Structure-preserving contrastive learning for spatial time seriesCode0
SIGMA: Sheaf-Informed Geometric Multi-Agent PathfindingCode0
On the use of Performer and Agent Attention for Spoken Language Identification—0
Target Speaker Lipreading by Audio-Visual Self-Distillation Pretraining and Speaker Adaptation—0
Synergistic Effects of Knowledge Distillation and Structured Pruning for Self-Supervised Speech Models—0
Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal imagesCode0
ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution ShiftsCode1
Learning Street View Representations with Spatiotemporal ContrastCode0
Unified Approaches in Self-Supervised Event Stream Modeling: Progress and Prospects—0
A Foundational Brain Dynamics Model via Stochastic Optimal Control—0
Singing Voice Conversion with Accompaniment Using Self-Supervised Representation-Based Melody Features—0
Self-Supervised Learning for Pre-training Capsule Networks: Overcoming Medical Imaging Dataset Challenges—0
Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake DetectionCode1
MetaFE-DE: Learning Meta Feature Embedding for Depth Estimation from Monocular Endoscopic Images—0
Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation EmbeddingsCode0
Metis: A Foundation Speech Generation Model with Masked Generative Pre-trainingCode9
Astromer 2—0
A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation—0
BRIDLE: Generalized Self-supervised Learning with QuantizationCode0
Self-Supervised Convolutional Audio Models are Flexible Acoustic Feature Learners: A Domain Specificity and Transfer-Learning StudyCode0
Particle Trajectory Representation Learning with Masked Point Modeling—0
EmoTalkingGaussian: Continuous Emotion-conditioned Talking Head Synthesis—0
Leveraging Joint Predictive Embedding and Bayesian Inference in Graph Self Supervised LearningCode0
A Checks-and-Balances Framework for Context-Aware Ethical AI Alignment—0
Language Bias in Self-Supervised Learning For Automatic Speech Recognition—0
Self-Supervised Learning Using Nonlinear Dependence—0
Clustering Properties of Self-Supervised Learning—0
Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling—0
Temperature-Free Loss Function for Contrastive Learning—0
ViT-2SPN: Vision Transformer-based Dual-Stream Self-Supervised Pretraining Networks for Retinal OCT ClassificationCode0
Few Edges Are Enough: Few-Shot Network Attack Detection with Graph Neural Networks—0
Beyond-Labels: Advancing Open-Vocabulary Segmentation With Vision-Language Models—0
A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks—0
Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0—0
Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models—0
Efficient Self-Supervised Grading of Prostate Cancer Pathology—0
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?—0
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open ChallengesCode1
ACT-JEPA: Joint-Embedding Predictive Architecture Improves Policy Representation Learning—0
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly DetectionCode0
A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation—0
Towards Intelligent Design: A Self-driven Framework for Collocated Clothing Synthesis Leveraging Fashion Styles and Textures—0
PromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments—0
MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender SystemsCode1
PointOBB-v3: Expanding Performance Boundaries of Single Point-Supervised Oriented Object DetectionCode2
DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition—0
A Probabilistic Model for Self-Supervised Learning—0
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer AnalysisCode0
A Novel Tracking Framework for Devices in X-ray Leveraging Supplementary Cue-Driven Self-Supervised Features—0
A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications—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