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 2351–2400 of 5044 papers

TitleStatusHype
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
Enhancing CTR Prediction through Sequential Recommendation Pre-training: Introducing the SRP4CTR Framework—0
A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation—0
Enhancing Contrastive Learning for Retinal Imaging via Adjusted Augmentation Scales—0
Enhancing Child Vocalization Classification with Phonetically-Tuned Embeddings for Assisting Autism Diagnosis—0
Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0—0
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition—0
Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning—0
Enhancing 2D Representation Learning with a 3D Prior—0
A Selective Survey on Versatile Knowledge Distillation Paradigm for Neural Network Models—0
EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation—0
Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security—0
CbwLoss: Constrained Bidirectional Weighted Loss for Self-supervised Learning of Depth and Pose—0
A Comparison of Deep Learning MOS Predictors for Speech Synthesis Quality—0
Enhanced Few-Shot Class-Incremental Learning via Ensemble Models—0
CBF-AFA: Chunk-Based Multi-SSL Fusion for Automatic Fluency Assessment—0
3D Pre-training improves GNNs for Molecular Property Prediction—0
Model Extraction Attack against Self-supervised Speech Models—0
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning—0
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation—0
End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch—0
Causal Speech Enhancement with Predicting Semantics based on Quantized Self-supervised Learning Features—0
Art Style Classification with Self-Trained Ensemble of AutoEncoding Transformations—0
End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data—0
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
Causality and "In-the-Wild" Video-Based Person Re-ID: A Survey—0
End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge—0
Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion—0
Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data—0
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning—0
Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling—0
Encoding Event-Based Gesture Data With a Hybrid SNN Guided Variational Auto-encoder—0
Encoders and Ensembles for Task-Free Continual Learning—0
Encoder-Decoder Networks for Self-Supervised Pretraining and Downstream Signal Bandwidth Regression on Digital Antenna Arrays—0
CASTing Your Model: Learning to Localize Improves Self-Supervised Representations—0
Enabling the Network to Surf the Internet—0
CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing—0
CASSL: Curriculum Accelerated Self-Supervised Learning—0
A Revisit of the Normalized Eight-Point Algorithm and A Self-Supervised Deep Solution—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
A Comparative Study of Voice Conversion Models with Large-Scale Speech and Singing Data: The T13 Systems for the Singing Voice Conversion Challenge 2023—0
CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification—0
Empirical Studies on the Convergence of Feature Spaces in Deep 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