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 3551–3600 of 5044 papers

TitleStatusHype
Generalizable Re-Identification from Videos with Cycle Association—0
On minimal variations for unsupervised representation learning—0
ProtoX: Explaining a Reinforcement Learning Agent via PrototypingCode0
Understanding the properties and limitations of contrastive learning for Out-of-Distribution detection—0
Privacy-Preserving Models for Legal Natural Language ProcessingCode0
Local Manifold Augmentation for Multiview Semantic Consistency—0
Self-Supervised Learning for Speech Enhancement through SynthesisCode0
Biased Self-supervised learning for ASR—0
EquiMod: An Equivariance Module to Improve Self-Supervised LearningCode0
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild—0
More Speaking or More Speakers?—0
Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised LearningCode0
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?Code0
Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound—0
Self-Supervised Intensity-Event Stereo Matching—0
Avoid Overthinking in Self-Supervised Models for Speech Recognition—0
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings—0
Investigating Content-Aware Neural Text-To-Speech MOS Prediction Using Prosodic and Linguistic Features—0
RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images—0
Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings—0
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
Saliency Can Be All You Need In Contrastive Self-Supervised Learning—0
DyG2Vec: Efficient Representation Learning for Dynamic GraphsCode0
Pair DETR: Contrastive Learning Speeds Up DETR Training—0
Learning Dependencies of Discrete Speech Representations with Neural Hidden Markov Models—0
Relating Human Perception of Musicality to Prediction in a Predictive Coding ModelCode0
Spectrograms Are Sequences of PatchesCode0
Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer—0
FUSSL: Fuzzy Uncertain Self Supervised Learning—0
Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition—0
Self-Supervised Training of Speaker Encoder with Multi-Modal Diverse Positive Pairs—0
MAEEG: Masked Auto-encoder for EEG Representation Learning—0
PatchRot: A Self-Supervised Technique for Training Vision TransformersCode0
Training Autoregressive Speech Recognition Models with Limited in-domain Supervision—0
Is Multi-Task Learning an Upper Bound for Continual Learning?—0
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image SegmentationCode0
Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input—0
UFO2: A unified pre-training framework for online and offline speech recognition—0
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks—0
Robust Self-Supervised Learning with Lie Groups—0
Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices—0
Self-supervised Rewiring of Pre-trained Speech Encoders: Towards Faster Fine-tuning with Less Labels in Speech ProcessingCode0
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control—0
Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation—0
Self-supervised Amodal Video Object SegmentationCode0
Modal-specific Pseudo Query Generation for Video Corpus Moment RetrievalCode0
Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning—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
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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