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 301–350 of 5044 papers

TitleStatusHype
Deep Self-Supervised Representation Learning for Free-Hand SketchCode1
Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake DetectionCode1
Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop FeedbackCode1
Deeper into Self-Supervised Monocular Indoor Depth EstimationCode1
Bootstrap Your Own Latent - A New Approach to Self-Supervised LearningCode1
Boundary-aware Self-supervised Learning for Video Scene SegmentationCode1
Can Vision Transformers Learn without Natural Images?Code1
Deep learning powered real-time identification of insects using citizen science dataCode1
Deep Unfolded Tensor Robust PCA with Self-supervised LearningCode1
Mitigating Memorization of Noisy Labels via Regularization between RepresentationsCode1
Differentiable Raycasting for Self-supervised Occupancy ForecastingCode1
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical ImagingCode1
Boosting Self-Supervised Embeddings for Speech EnhancementCode1
Deciphering and integrating invariants for neural operator learning with various physical mechanismsCode1
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the LeastCode1
Boosting Generalization in Bio-Signal Classification by Learning the Phase-Amplitude CouplingCode1
Data-Efficient Reinforcement Learning with Self-Predictive RepresentationsCode1
Large-Scale Representation Learning on Graphs via BootstrappingCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
EXAONEPath 1.0 Patch-level Foundation Model for PathologyCode1
Boosting Contrastive Self-Supervised Learning with False Negative CancellationCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial RobustnessCode1
Physics-informed Temporal Alignment for Auto-regressive PDE Foundation ModelsCode1
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
A foundation model for generalizable disease diagnosis in chest X-ray imagesCode1
data2vec-aqc: Search for the right Teaching Assistant in the Teacher-Student training setupCode1
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and MappingCode1
A comprehensive survey on deep active learning in medical image analysisCode1
Blockwise Self-Supervised Learning at ScaleCode1
3D Self-Supervised Methods for Medical ImagingCode1
A Fast Knowledge Distillation Framework for Visual RecognitionCode1
Bootstrapping Autonomous Driving Radars with Self-Supervised LearningCode1
Data Augmentation for Object Detection via Differentiable Neural RenderingCode1
D2C: Diffusion-Decoding Models for Few-Shot Conditional GenerationCode1
Big Self-Supervised Models Advance Medical Image ClassificationCode1
D2C: Diffusion-Denoising Models for Few-shot Conditional GenerationCode1
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image SegmentationCode1
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual RepresentationsCode1
Crowdsourced 3D Mapping: A Combined Multi-View Geometry and Self-Supervised Learning ApproachCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from ImageCode1
DABS: A Domain-Agnostic Benchmark for Self-Supervised LearningCode1
A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-IdentificationCode1
Object Segmentation Without Labels with Large-Scale Generative ModelsCode1
Bidirectional Learning for Domain Adaptation of Semantic SegmentationCode1
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resectionsCode1
Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular DomainsCode1
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving ScenariosCode1
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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