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 2451–2500 of 5044 papers

TitleStatusHype
Are foundation models efficient for medical image segmentation?—0
Efficient Training of Self-Supervised Speech Foundation Models on a Compute Budget—0
A Collective Learning Framework to Boost GNN Expressiveness—0
Multi-Parameter Molecular MRI Quantification using Physics-Informed Self-Supervised Learning—0
Multi-Scale Neighborhood Occupancy Masked Autoencoder for Self-Supervised Learning in LiDAR Point Clouds—0
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition—0
Efficient Test-Time Prompt Tuning for Vision-Language Models—0
Can representation learning for multimodal image registration be improved by supervision of intermediate layers?—0
3D Molecular Geometry Analysis with 2D Graphs—0
Efficient Self-Supervised Grading of Prostate Cancer Pathology—0
Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis—0
Multi-OCT-SelfNet: Integrating Self-Supervised Learning with Multi-Source Data Fusion for Enhanced Multi-Class Retinal Disease Classification—0
Efficient Self-supervised Continual Learning with Progressive Task-correlated Layer Freezing—0
Can Masked Autoencoders Also Listen to Birds?—0
Are all negatives created equal in contrastive instance discrimination?—0
Efficient onboard multi-task AI architecture based on self-supervised learning—0
Can large-scale vocoded spoofed data improve speech spoofing countermeasure with a self-supervised front end?—0
Multi-network Contrastive Learning Based on Global and Local Representations—0
Efficient Medical Image Assessment via Self-supervised Learning—0
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations—0
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?—0
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations—0
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning—0
Training on the Fly: On-device Self-supervised Learning aboard Nano-drones within 20 mW—0
Multi-object tracking with self-supervised associating network—0
Multi-organ Self-supervised Contrastive Learning for Breast Lesion Segmentation—0
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation—0
Multi-source Few-shot Domain Adaptation—0
New Test-Time Scenario for Biosignal: Concept and Its Approach—0
Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach—0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
Can bidirectional encoder become the ultimate winner for downstream applications of foundation models?—0
CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification—0
Efficiency-oriented approaches for self-supervised speech representation learning—0
Adversarial defense for automatic speaker verification by cascaded self-supervised learning models—0
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages—0
EEG-SCMM: Soft Contrastive Masked Modeling for Cross-Corpus EEG-Based Emotion Recognition—0
C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification—0
Graph Contrastive Learning with Cross-view Reconstruction—0
EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model—0
EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography—0
EDITnet: A Lightweight Network for Unsupervised Domain Adaptation in Speaker Verification—0
Edit as You See: Image-guided Video Editing via Masked Motion Modeling—0
3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI—0
EchoSpike Predictive Plasticity: An Online Local Learning Rule for Spiking Neural Networks—0
BYOLMed3D: Self-Supervised Representation Learning of Medical Videos using Gradient Accumulation Assisted 3D BYOL Framework—0
Echocardiogram Foundation Model -- Application 1: Estimating Ejection Fraction—0
Adversarial Contrastive Self-Supervised Learning—0
EchoApex: A General-Purpose Vision Foundation Model for Echocardiography—0
ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal—0
Show:102550
← PrevPage 50 of 101Next →
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