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
Tripartite: Tackle Noisy Labels by a More Precise Partition—0
Towards better understanding and better generalization of few-shot classification in histology images with contrastive learningCode1
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment ContrastCode1
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?—0
Masked prediction tasks: a parameter identifiability view—0
Survey on Self-supervised Representation Learning Using Image Transformations—0
Self-Supervised Representation Learning via Latent Graph Prediction—0
Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised trainingCode1
Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-TrainingCode1
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision—0
Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning—0
Misinformation Detection in Social Media Video Posts—0
Learning Contextually Fused Audio-visual Representations for Audio-visual Speech Recognition—0
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning—0
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness—0
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning—0
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features—0
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data ReleaseCode0
AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation—0
Classification of Microscopy Images of Breast Tissue: Region Duplication based Self-Supervision vs. Off-the Shelf Deep Representations—0
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?Code1
Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning—0
Investigating Power laws in Deep Representation Learning—0
Using Navigational Information to Learn Visual Representations—0
Energy-Based Contrastive Learning of Visual RepresentationsCode1
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RLCode1
Results and findings of the 2021 Image Similarity Challenge—0
How to Understand Masked Autoencoders—0
Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation—0
Simple Control Baselines for Evaluating Transfer Learning—0
Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech RecognitionCode1
Context Autoencoder for Self-Supervised Representation LearningCode2
Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship AttributionCode0
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
Graph Self-supervised Learning with Accurate Discrepancy LearningCode1
Backdoor Defense via Decoupling the Training ProcessCode1
Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning—0
Intent Contrastive Learning for Sequential RecommendationCode1
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series—0
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type ClassificationCode0
Self-supervised Learning with Random-projection Quantizer for Speech RecognitionCode1
Understanding The Robustness of Self-supervised Learning Through Topic Modeling—0
AtmoDist: Self-supervised Representation Learning for Atmospheric DynamicsCode0
Relative Position Prediction as Pre-training for Text Encoders—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
Adversarial Masking for Self-Supervised LearningCode1
Contrastive Learning from Demonstrations—0
Graph Representation Learning via Aggregation EnhancementCode1
Self Semi Supervised Neural Architecture Search for Semantic Segmentation—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