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 4651–4700 of 5044 papers

TitleStatusHype
Adversarial Momentum-Contrastive Pre-TrainingCode0
Self-supervised self-supervision by combining deep learning and probabilistic logic—0
Motif-Driven Contrastive Learning of Graph Representations—0
Self-Supervised Learning for Visual Summary Identification in Scientific Publications—0
Self-supervised monocular depth estimation from oblique UAV videosCode0
Leveraging Meta-path Contexts for Classification in Heterogeneous Information Networks—0
Self-supervised Learning with Fully Convolutional Networks—0
The effectiveness of unsupervised subword modeling with autoregressive and cross-lingual phone-aware networks—0
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees—0
Exploiting Behavioral Consistence for Universal User Representation—0
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning—0
CASTing Your Model: Learning to Localize Improves Self-Supervised Representations—0
Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histology—0
Temporal-Aware Self-Supervised Learning for 3D Hand Pose and Mesh Estimation in Videos—0
Art Style Classification with Self-Trained Ensemble of AutoEncoding Transformations—0
Is It a Plausible Colour? UCapsNet for Image ColourisationCode0
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning—0
Rethinking supervised learning: insights from biological learning and from calling it by its name—0
Cross-Loss Influence Functions to Explain Deep Network RepresentationsCode0
Self-Supervised Learning for Pairwise Data Refinement—0
Latent Programmer: Discrete Latent Codes for Program Synthesis—0
Intermediate Self-supervised Learning for Machine Translation Quality Estimation—0
SIR: Self-supervised Image Rectification via Seeing the Same Scene from Multiple Different Lenses—0
A Selective Survey on Versatile Knowledge Distillation Paradigm for Neural Network Models—0
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment—0
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation—0
Can Temporal Information Help with Contrastive Self-Supervised Learning?—0
PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate DistributionsCode0
Grafit: Learning fine-grained image representations with coarse labels—0
Variational Monocular Depth Estimation for Reliability Prediction—0
Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach—0
Learning Object-Centric Video Models by Contrasting Sets—0
Self-Supervised learning with cross-modal transformers for emotion recognition—0
Classification by Attention: Scene Graph Classification with Prior Knowledge—0
Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervisionCode0
Robot Gaining Accurate Pouring Skills through Self-Supervised Learning and Generalization—0
Self-Supervised Physics-Guided Deep Learning Reconstruction For High-Resolution 3D LGE CMR—0
Combining Self-Supervised and Supervised Learning with Noisy Labels—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
ActBERT: Learning Global-Local Video-Text RepresentationsCode0
Discriminative, Generative and Self-Supervised Approaches for Target-Agnostic Learning—0
Unsupervised Learning of Dense Visual Representations—0
Self-Supervised Out-of-Distribution Detection in Brain CT Scans—0
A Self-supervised Learning System for Object Detection in Videos Using Random Walks on GraphsCode0
Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables—0
Graph Neural Networks for Distributed Linear-Quadratic Control—0
Towards Domain-Agnostic Contrastive Learning—0
Self-Supervised Learning from Contrastive Mixtures for Personalized Speech EnhancementCode0
Self-Supervised Learning for Biological Sample Localization in 3D Tomographic Images—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