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 3501–3550 of 5044 papers

TitleStatusHype
Model Extraction Attack against Self-supervised Speech Models—0
On the Power of Foundation Models—0
Survey on Self-Supervised Multimodal Representation Learning and Foundation Models—0
Semi-supervised binary classification with latent distance learning—0
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs—0
A Theoretical Study of Inductive Biases in Contrastive Learning—0
WSSL: Weighted Self-supervised Learning Framework For Image-inpaintingCode0
Ladder Siamese Network: a Method and Insights for Multi-level Self-Supervised Learning—0
Link Prediction with Non-Contrastive LearningCode0
BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular RepresentationCode0
Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI—0
Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding—0
Self-Supervised Learning based on Heat Equation—0
Reason from Context with Self-supervised Learning—0
On the Transferability of Visual Features in Generalized Zero-Shot LearningCode0
CLAWSAT: Towards Both Robust and Accurate Code ModelsCode0
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning ParadigmCode0
Deep Projective Rotation Estimation through Relative Supervision—0
Simultaneously Learning Robust Audio Embeddings and balanced Hash codes for Query-by-Example—0
ESTAS: Effective and Stable Trojan Attacks in Self-supervised Encoders with One Target Unlabelled Sample—0
Local Contrastive Feature learning for Tabular Data—0
Exploring WavLM on Speech Enhancement—0
Weighted Ensemble Self-Supervised Learning—0
CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers—0
Self-Supervised Visual Representation Learning via Residual Momentum—0
Is Smaller Always Faster? Tradeoffs in Compressing Self-Supervised Speech TransformersCode0
Balanced Deep CCA for Bird Vocalization Detection—0
SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery—0
Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation—0
Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite ImageryCode0
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle—0
Pretraining ECG Data with Adversarial Masking Improves Model Generalizability for Data-Scarce TasksCode0
Homomorphic Self-Supervised Learning—0
Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning—0
AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging—0
Improving Children's Speech Recognition by Fine-tuning Self-supervised Adult Speech RepresentationsCode0
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning—0
Information-guided pixel augmentation for pixel-wise contrastive learning—0
LLEDA -- Lifelong Self-Supervised Domain Adaptation—0
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation—0
An Adapter based Multi-label Pre-training for Speech Separation and Enhancement—0
The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia ClassificationCode0
A Survey of Knowledge Enhanced Pre-trained Language Models—0
Contrastive Self-Supervised Learning for Skeleton Representations—0
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition—0
Self-supervised learning of audio representations using angular contrastive lossCode0
Flaky Performances when Pretraining on Relational Databases—0
Interactive Feature Embedding for Infrared and Visible Image Fusion—0
Speech separation with large-scale self-supervised learning—0
Pushing the limits of self-supervised speaker verification using regularized distillation framework—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