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 3501–3550 of 5044 papers

TitleStatusHype
Enhancing Child Vocalization Classification with Phonetically-Tuned Embeddings for Assisting Autism Diagnosis—0
Enhancing Contrastive Learning for Retinal Imaging via Adjusted Augmentation Scales—0
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework—0
Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder—0
Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling—0
Enhancing expressivity transfer in textless speech-to-speech translation—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
Enhancing Graph Contrastive Learning with Node Similarity—0
Enhancing Graph Self-Supervised Learning with Graph Interplay—0
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Enhancing Representations through Heterogeneous Self-Supervised Learning—0
Enhancing SAR Object Detection with Self-Supervised Pre-training on Masked Auto-Encoders—0
Enhancing Speech Emotion Recognition through Segmental Average Pooling of Self-Supervised Learning Features—0
Enhancing the Generalization Capability of Skin Lesion Classification Models with Active Domain Adaptation Methods—0
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation—0
Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning—0
Ensembles and Encoders for Task-Free Continual Learning—0
EnSiam: Self-Supervised Learning With Ensemble Representations—0
Environment Predictive Coding for Embodied Agents—0
Environment Predictive Coding for Visual Navigation—0
Equivariance-based self-supervised learning for audio signal recovery from clipped measurements—0
Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting—0
Equivariant Representation Learning for Augmentation-based Self-Supervised Learning via Image Reconstruction—0
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations—0
Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning—0
ES3: Evolving Self-Supervised Learning of Robust Audio-Visual Speech Representations—0
ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning—0
ESTAS: Effective and Stable Trojan Attacks in Self-supervised Encoders with One Target Unlabelled Sample—0
Estimating Galactic Distances From Images Using Self-supervised Representation Learning—0
Eta-WavLM: Efficient Speaker Identity Removal in Self-Supervised Speech Representations Using a Simple Linear Equation—0
ETP: Learning Transferable ECG Representations via ECG-Text Pre-training—0
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization—0
Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data—0
Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for Robustness, Generalizability, and Multi-Domain Impact—0
Evaluating the fairness of fine-tuning strategies in self-supervised learning—0
Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery—0
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging—0
Evaluating Visual Explanations of Attention Maps for Transformer-based Medical Imaging—0
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment—0
Event Camera Data Dense Pre-training—0
Event Camera Data Pre-training—0
EventPoint: Self-Supervised Interest Point Detection and Description for Event-based Camera—0
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
Evolutionary algorithms meet self-supervised learning: a comprehensive survey—0
Evolutionary Augmentation Policy Optimization for Self-supervised Learning—0
Evolved Part Masking for Self-Supervised Learning—0
Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning—0
Exemplar Learning for Medical Image Segmentation—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—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