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 3451–3500 of 5044 papers

TitleStatusHype
Edit as You See: Image-guided Video Editing via Masked Motion Modeling—0
EDITnet: A Lightweight Network for Unsupervised Domain Adaptation in Speaker Verification—0
EDMAE: An Efficient Decoupled Masked Autoencoder for Standard View Identification in Pediatric Echocardiography—0
EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model—0
EEG-SCMM: Soft Contrastive Masked Modeling for Cross-Corpus EEG-Based Emotion Recognition—0
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages—0
Efficiency-oriented approaches for self-supervised speech representation learning—0
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks—0
Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach—0
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations—0
Efficient Medical Image Assessment via Self-supervised Learning—0
Efficient onboard multi-task AI architecture based on self-supervised learning—0
Efficient Self-supervised Continual Learning with Progressive Task-correlated Layer Freezing—0
Efficient Self-Supervised Grading of Prostate Cancer Pathology—0
Efficient Test-Time Prompt Tuning for Vision-Language Models—0
Efficient Training of Self-Supervised Speech Foundation Models on a Compute Budget—0
Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer—0
Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling—0
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning—0
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following—0
Embodied Image Captioning: Self-supervised Learning Agents for Spatially Coherent Image Descriptions—0
Embodied Self-supervised Learning by Coordinated Sampling and Training—0
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs—0
Emerging Property of Masked Token for Effective Pre-training—0
Emo-bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition—0
EmoGen: Eliminating Subjective Bias in Emotional Music Generation—0
EmoReg: Directional Latent Vector Modeling for Emotional Intensity Regularization in Diffusion-based Voice Conversion—0
EmoTalkingGaussian: Continuous Emotion-conditioned Talking Head Synthesis—0
Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition—0
Emotional Conversation: Empowering Talking Faces with Cohesive Expression, Gaze and Pose Generation—0
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions—0
Emotion-Aware Speech Self-Supervised Representation Learning with Intensity Knowledge—0
EMP: Enhance Memory in Data Pruning—0
Empirical Studies on the Convergence of Feature Spaces in Deep Learning—0
Enabling the Network to Surf the Internet—0
Encoder-Decoder Networks for Self-Supervised Pretraining and Downstream Signal Bandwidth Regression on Digital Antenna Arrays—0
Encoders and Ensembles for Task-Free Continual Learning—0
Encoding Event-Based Gesture Data With a Hybrid SNN Guided Variational Auto-encoder—0
Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling—0
Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion—0
End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge—0
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection—0
End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data—0
End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch—0
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation—0
Enhanced Few-Shot Class-Incremental Learning via Ensemble Models—0
Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security—0
EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation—0
Enhancing 2D Representation Learning with a 3D Prior—0
Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0—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