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 3101–3150 of 5044 papers

TitleStatusHype
BOE-ViT: Boosting Orientation Estimation with Equivariance in Self-Supervised 3D Subtomogram Alignment—0
Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling—0
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup—0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification—0
Boosting Search Engines with Interactive Agents—0
Boosting Self-Supervised Learning via Knowledge Transfer—0
Boosting Supervised Learning Performance with Co-training—0
Boosting Supervision with Self-Supervision for Few-shot Learning—0
A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection—0
Bootstrapped Representation Learning on Graphs—0
Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation—0
Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement—0
Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences—0
Bootstrap Your Own Variance—0
Boundary-aware Pre-training for Video Scene Segmentation—0
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection—0
BrainMAE: A Region-aware Self-supervised Learning Framework for Brain Signals—0
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning—0
Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis—0
Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning—0
Bridging Domain Gap of Point Cloud Representations via Self-Supervised Geometric Augmentation—0
Spectal Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning—0
Bridging the Gap between Continuous and Informative Discrete Representations by Random Product Quantization—0
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision—0
Brief Introduction to Contrastive Learning Pretext Tasks for Visual Representation—0
Bringing CLIP to the Clinic: Dynamic Soft Labels and Negation-Aware Learning for Medical Analysis—0
Broad Adversarial Training with Data Augmentation in the Output Space—0
BTranspose: Bottleneck Transformers for Human Pose Estimation with Self-Supervised Pre-Training—0
Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds—0
Building3D: A Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds—0
Building 6G Radio Foundation Models with Transformer Architectures—0
BYOLMed3D: Self-Supervised Representation Learning of Medical Videos using Gradient Accumulation Assisted 3D BYOL Framework—0
C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification—0
CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification—0
Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?—0
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?—0
Can large-scale vocoded spoofed data improve speech spoofing countermeasure with a self-supervised front end?—0
Can Masked Autoencoders Also Listen to Birds?—0
Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis—0
Can representation learning for multimodal image registration be improved by supervision of intermediate layers?—0
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition—0
Can Temporal Information Help with Contrastive Self-Supervised Learning?—0
Can We Ignore Labels In Out of Distribution Detection?—0
Captured by Captions: On Memorization and its Mitigation in CLIP Models—0
CardiGraphormer: Unveiling the Power of Self-Supervised Learning in Revolutionizing Drug Discovery—0
CardOOD: Robust Query-driven Cardinality Estimation under Out-of-Distribution—0
CARL-G: Clustering-Accelerated Representation Learning on Graphs—0
Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition—0
Cascade Network for Self-Supervised Monocular Depth Estimation—0
CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification—0
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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