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 3201–3250 of 5044 papers

TitleStatusHype
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation—0
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation—0
Compact Speech Translation Models via Discrete Speech Units Pretraining—0
Comparing Foundation Models using Data Kernels—0
Comparison of Speech Representations for the MOS Prediction System—0
Complex Mixer for MedMNIST Classification Decathlon—0
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma—0
Computational Pathology at Health System Scale -- Self-Supervised Foundation Models from Three Billion Images—0
Concept Representation Learning with Contrastive Self-Supervised Learning—0
CONCSS: Contrastive-based Context Comprehension for Dialogue-appropriate Prosody in Conversational Speech Synthesis—0
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning—0
Conditional Augmentation for Generative Modeling—0
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning—0
Conditional Hybrid GAN for Sequence Generation—0
Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning—0
Connecting the Dots: Inferring Patent Phrase Similarity with Retrieved Phrase Graphs—0
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks—0
Consistency Regularization Can Improve Robustness to Label Noise—0
Consistent 3D Hand Reconstruction in Video via self-supervised Learning—0
Constrained Mean Shift for Representation Learning—0
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection—0
Contactless Pulse Estimation Leveraging Pseudo Labels and Self-Supervision—0
Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network—0
Context-Aware Self-Supervised Learning of Whole Slide Images—0
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space—0
Contextualized and Generalized Sentence Representations by Contrastive Self-Supervised Learning: A Case Study on Discourse Relation Analysis—0
Contextures: The Mechanism of Representation Learning—0
Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation—0
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction—0
Continual Robot Learning using Self-Supervised Task Inference—0
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images—0
Continual Self-Supervised Learning with Masked Autoencoders in Remote Sensing—0
Continual Vision-Language Representation Learning with Off-Diagonal Information—0
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning—0
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
Contrast and Order Representations for Video Self-Supervised Learning—0
Contrastive Abstraction for Reinforcement Learning—0
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods—0
Learning Video Representations using Contrastive Bidirectional Transformer—0
Contrastive Continuity on Augmentation Stability Rehearsal for Continual Self-Supervised Learning—0
Contrastive Domain Adaptation—0
Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning—0
Contrastive General Graph Matching with Adaptive Augmentation Sampling—0
Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning—0
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency—0
Contrastive Learning from Demonstrations—0
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series—0
Contrastive learning, multi-view redundancy, and linear models—0
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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