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 2301–2350 of 5044 papers

TitleStatusHype
ES3: Evolving Self-Supervised Learning of Robust Audio-Visual Speech Representations—0
Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning—0
CHAPTER: Exploiting Convolutional Neural Network Adapters for Self-supervised Speech Models—0
A Self-Supervised Learning of a Foundation Model for Analog Layout Design Automation—0
Chaos is a Ladder: A New Understanding of Contrastive Learning—0
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations—0
A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders—0
Equivariant Representation Learning for Augmentation-based Self-Supervised Learning via Image Reconstruction—0
Adversarial Transfer of Pose Estimation Regression—0
Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting—0
Equivariance-based self-supervised learning for audio signal recovery from clipped measurements—0
Channel Self-Supervision for Online Knowledge Distillation—0
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping—0
3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion—0
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion—0
Environment Predictive Coding for Visual Navigation—0
Environment Predictive Coding for Embodied Agents—0
Challenging Gradient Boosted Decision Trees with Tabular Transformers for Fraud Detection at Booking.com—0
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking—0
Migrate Demographic Group For Fair GNNs—0
EnSiam: Self-Supervised Learning With Ensemble Representations—0
Ensembles and Encoders for Task-Free Continual Learning—0
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images—0
Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning—0
A Self-supervised GAN for Unsupervised Few-shot Object Recognition—0
Meta Transfer of Self-Supervised Knowledge: Foundation Model in Action for Post-Traumatic Epilepsy Prediction—0
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation—0
Enhancing the Generalization Capability of Skin Lesion Classification Models with Active Domain Adaptation Methods—0
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data—0
Enhancing Speech Emotion Recognition through Segmental Average Pooling of Self-Supervised Learning Features—0
Enhancing SAR Object Detection with Self-Supervised Pre-training on Masked Auto-Encoders—0
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data—0
Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training—0
Enhancing Representations through Heterogeneous Self-Supervised Learning—0
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention—0
Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks—0
Cell-ontology guided transcriptome foundation model—0
A Self-Supervised Framework for Space Object Behaviour Characterisation—0
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning—0
Enhancing Graph Self-Supervised Learning with Graph Interplay—0
A self-supervised framework for learning whole slide representations—0
Enhancing Graph Contrastive Learning with Node Similarity—0
Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models—0
CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning—0
Adversarial Semi-Supervised Multi-Domain Tracking—0
Lossy Neural Compression for Geospatial Analytics: A Review—0
Enhancing expressivity transfer in textless speech-to-speech translation—0
Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling—0
CEIR: Concept-based Explainable Image Representation Learning—0
Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder—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