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 2801–2850 of 5044 papers

TitleStatusHype
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings—0
Self-Supervised Intensity-Event Stereo Matching—0
Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings—0
Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentationCode1
DUEL: Adaptive Duplicate Elimination on Working Memory for Self-Supervised Learning—0
Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning modelsCode0
DyG2Vec: Efficient Representation Learning for Dynamic GraphsCode0
A simple, efficient and scalable contrastive masked autoencoder for learning visual representationsCode1
Saliency Can Be All You Need In Contrastive Self-Supervised Learning—0
Learning Dependencies of Discrete Speech Representations with Neural Hidden Markov Models—0
Pair DETR: Contrastive Learning Speeds Up DETR Training—0
Relating Human Perception of Musicality to Prediction in a Predictive Coding ModelCode0
Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer—0
FUSSL: Fuzzy Uncertain Self Supervised Learning—0
Spectrograms Are Sequences of PatchesCode0
Self-Supervised Learning with Multi-View Rendering for 3D Point Cloud AnalysisCode1
MAEEG: Masked Auto-encoder for EEG Representation Learning—0
PatchRot: A Self-Supervised Technique for Training Vision TransformersCode0
Facial Video-based Remote Physiological Measurement via Self-supervised LearningCode1
Training Autoregressive Speech Recognition Models with Limited in-domain Supervision—0
Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition—0
Open-vocabulary Semantic Segmentation with Frozen Vision-Language ModelsCode1
Self-Supervised Training of Speaker Encoder with Multi-Modal Diverse Positive Pairs—0
Multitask Detection of Speaker Changes, Overlapping Speech and Voice Activity Using wav2vec 2.0Code1
UFO2: A unified pre-training framework for online and offline speech recognition—0
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image SegmentationCode0
Is Multi-Task Learning an Upper Bound for Continual Learning?—0
Broken Neural Scaling LawsCode1
Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input—0
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks—0
MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property PredictionCode1
Robust Self-Supervised Learning with Lie Groups—0
Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices—0
Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised LearningCode2
Self-supervised Rewiring of Pre-trained Speech Encoders: Towards Faster Fine-tuning with Less Labels in Speech ProcessingCode0
Contrastive Representation Learning for Gaze EstimationCode1
Self-supervised Sparse Representation for Video Anomaly DetectionCode1
Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning—0
Self-supervised Amodal Video Object SegmentationCode0
Neural Eigenfunctions Are Structured Representation LearnersCode1
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control—0
Modal-specific Pseudo Query Generation for Video Corpus Moment RetrievalCode0
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation—0
Self-supervised Graph-based Point-of-interest Recommendation—0
Self-Supervised Pretraining on Satellite Imagery: a Case Study on Label-Efficient Vehicle Detection—0
Evidence of Vocal Tract Articulation in Self-Supervised Learning of SpeechCode1
SS-VAERR: Self-Supervised Apparent Emotional Reaction Recognition from Video—0
Self-Supervised Learning with Masked Image Modeling for Teeth Numbering, Detection of Dental Restorations, and Instance Segmentation in Dental Panoramic RadiographsCode1
Towards Sustainable Self-supervised LearningCode1
SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy GradingCode1
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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