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 3701–3750 of 5044 papers

TitleStatusHype
Generalised Co-Salient Object Detection—0
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning—0
Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Framework—0
Generalizable Re-Identification from Videos with Cycle Association—0
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification—0
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework—0
Generating Music Medleys via Playing Music Puzzle Games—0
Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
Generative Deduplication For Socia Media Data Selection—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning—0
GenURL: A General Framework for Unsupervised Representation Learning—0
GEO-BLEU: Similarity Measure for Geospatial Sequences—0
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D—0
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning—0
Category-Adaptive Domain Adaptation for Semantic Segmentation—0
Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices—0
GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning—0
Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language Models—0
Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training—0
Grafit: Learning fine-grained image representations with coarse labels—0
Granularity-aware Adaptation for Image Retrieval over Multiple Tasks—0
Graph Adversarial Self-Supervised Learning—0
Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings—0
Graph Anomaly Detection via Adaptive Test-time Representation Learning across Out-of-Distribution Domains—0
Graph-Based Bidirectional Transformer Decision Threshold Adjustment Algorithm for Class-Imbalanced Molecular Data—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
GraphCL: Contrastive Self-Supervised Learning of Graph Representations—0
Graph Contrastive Learning with Generative Adversarial Network—0
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks—0
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs—0
Graph Masked Autoencoder for Spatio-Temporal Graph Learning—0
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning—0
Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach—0
Graph Neural Networks for Distributed Linear-Quadratic Control—0
Graph Neural Networks for Molecules—0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
Graph Neural Networks: Methods, Applications, and Opportunities—0
Graph Positional Autoencoders as Self-supervised Learners—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Graph Soft-Contrastive Learning via Neighborhood Ranking—0
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis—0
Green Steganalyzer: A Green Learning Approach to Image Steganalysis—0
Group Contrastive Self-Supervised Learning on Graphs—0
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning—0
Grow and Merge: A Unified Framework for Continuous Categories Discovery—0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—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