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 4351–4400 of 5044 papers

TitleStatusHype
Domain Adapting Ability of Self-Supervised Learning for Face Recognition—0
Towards Robust Graph Contrastive Learning—0
"Train one, Classify one, Teach one" -- Cross-surgery transfer learning for surgical step recognition—0
Enabling the Network to Surf the Internet—0
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation—0
NTopo: Mesh-free Topology Optimization using Implicit Neural RepresentationsCode1
UPRec: User-Aware Pre-training for Recommender Systems—0
Self-Supervised Learning of Graph Neural Networks: A Unified Review—0
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised LearningCode1
Contrastive Self-supervised Neural Architecture SearchCode0
Self-Supervised Learning via multi-Transformation Classification for Action Recognition—0
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image DenoisingCode1
Molecular Contrastive Learning of Representations via Graph Neural NetworksCode1
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample PredictionCode1
An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation—0
Contrastive Learning Inverts the Data Generating ProcessCode1
S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution CalibrationCode1
COCO-LM: Correcting and Contrasting Text Sequences for Language Model PretrainingCode1
Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents—0
Instance Localization for Self-supervised Detection PretrainingCode1
DOBF: A Deobfuscation Pre-Training Objective for Programming LanguagesCode1
MAPGN: MAsked Pointer-Generator Network for sequence-to-sequence pre-training—0
Zero-Shot Self-Supervised Learning for MRI ReconstructionCode1
Adversarial defense for automatic speaker verification by cascaded self-supervised learning models—0
Self-Supervised Multisensor Change Detection—0
Understanding self-supervised Learning Dynamics without Contrastive PairsCode2
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-TuningCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
Self-Supervised VQ-VAE for One-Shot Music Style TransferCode1
Self-supervised learning for fast and scalable time series hyper-parameter tuning—0
Conversational Query Rewriting with Self-supervised Learning—0
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment AnalysisCode1
Quantifying and Mitigating Privacy Risks of Contrastive LearningCode1
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning—0
Self-supervised driven consistency training for annotation efficient histopathology image analysisCode1
Custom Object Detection via Multi-Camera Self-Supervised Learning—0
Multi-Task Self-Supervised Pre-Training for Music Classification—0
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework—0
Echo-SyncNet: Self-supervised Cardiac View Synchronization in EchocardiographyCode1
Environment Predictive Coding for Embodied Agents—0
Civil Rephrases Of Toxic Texts With Self-Supervised TransformersCode1
On Scaling Contrastive Representations for Low-Resource Speech Recognition—0
Decoupling the Role of Data, Attention, and Losses in Multimodal TransformersCode0
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG dataCode1
SwingBot: Learning Physical Features from In-hand Tactile Exploration for Dynamic Swing-up Manipulation—0
Supervised Momentum Contrastive Learning for Few-Shot Classification—0
Unified Framework for Feature Extraction based on Contrastive Learning—0
Improving Few-Shot Learning with Auxiliary Self-Supervised Pretext TasksCode1
Self-Adaptive Training: Bridging Supervised and Self-Supervised LearningCode1
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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