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 3051–3100 of 5044 papers

TitleStatusHype
Automatic Detection of Out-of-body Frames in Surgical Videos for Privacy Protection Using Self-supervised Learning and Minimal Labels—0
Automatic Equalization for Individual Instrument Tracks Using Convolutional Neural Networks—0
Automatic Pronunciation Assessment using Self-Supervised Speech Representation Learning—0
Automatized Self-Supervised Learning for Skin Lesion Screening—0
Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback—0
Autoregressive Sequence Modeling for 3D Medical Image Representation—0
Autosen: improving automatic wifi human sensing through cross-modal autoencoder—0
AU-TTT: Vision Test-Time Training model for Facial Action Unit Detection—0
AuxMix: Semi-Supervised Learning with Unconstrained Unlabeled Data—0
AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations—0
A vector quantized masked autoencoder for audiovisual speech emotion recognition—0
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection—0
Avoid Overthinking in Self-Supervised Models for Speech Recognition—0
AWEncoder: Adversarial Watermarking Pre-trained Encoders in Contrastive Learning—0
Backdoor Attacks in the Supply Chain of Masked Image Modeling—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
Knowledge Distillation for Human Action Anticipation—0
Balanced Deep CCA for Bird Vocalization Detection—0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
BarlowTwins-CXR : Enhancing Chest X-Ray abnormality localization in heterogeneous data with cross-domain self-supervised learning—0
Bayesian Graph Contrastive Learning—0
Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment—0
Benchmarking Hierarchical Image Pyramid Transformer for the classification of colon biopsies and polyps in histopathology images—0
Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection—0
A Large-Scale Analysis on Self-Supervised Video Representation Learning—0
BERT vs ALBERT explained—0
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification—0
Better Reasoning Behind Classification Predictions with BERT for Fake News Detection—0
Beyond Accuracy: Statistical Measures and Benchmark for Evaluation of Representation from Self-Supervised Learning—0
Beyond Cosine Decay: On the effectiveness of Infinite Learning Rate Schedule for Continual Pre-training—0
Beyond H&E: Unlocking Pathological Insights with Polarization via Self-supervised Learning—0
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data—0
Beyond-Labels: Advancing Open-Vocabulary Segmentation With Vision-Language Models—0
Beyond Pairwise Correlations: Higher-Order Redundancies in Self-Supervised Representation Learning—0
Beyond Traditional Single Object Tracking: A Survey—0
Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning—0
Biased Self-supervised learning for ASR—0
Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing—0
Bilevel Optimized Implicit Neural Representation for Scan-Specific Accelerated MRI Reconstruction—0
BIM: Block-Wise Self-Supervised Learning with Masked Image Modeling—0
BinImg2Vec: Augmenting Malware Binary Image Classification with Data2Vec—0
BioSerenity-E1: a self-supervised EEG model for medical applications—0
Birth and Death of a Rose—0
BiSSL: Enhancing the Alignment Between Self-Supervised Pretraining and Downstream Fine-Tuning via Bilevel Optimization—0
Blending Low and High-Level Semantics of Time Series for Better Masked Time Series Generation—0
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting—0
Block-to-Scene Pre-training for Point Cloud Hybrid-Domain Masked Autoencoders—0
Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets—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