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 4001–4050 of 5044 papers

TitleStatusHype
Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image AnalysisCode0
Tackling Online One-Class Incremental Learning by Removing Negative Contrasts—0
Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions—0
Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels—0
The Challenges of Continuous Self-Supervised Learning—0
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition—0
Channel Self-Supervision for Online Knowledge Distillation—0
Text Transformations in Contrastive Self-Supervised Learning: A Review—0
Representation Uncertainty in Self-Supervised Learning as Variational Inference—0
Self-supervision through Random Segments with Autoregressive Coding (RandSAC)—0
Federated Self-Supervised Learning for Acoustic Event Classification—0
Longitudinal Self-Supervision for COVID-19 Pathology Quantification—0
Inferring topological transitions in pattern-forming processes with self-supervised learningCode0
Rethinking the optimization process for self-supervised model-driven MRI reconstruction—0
Contrastive Learning with Positive-Negative Frame Mask for Music Representation—0
Object discovery and representation networksCode0
Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography—0
Task-Agnostic Robust Representation Learning—0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot LearningCode0
Investigating self-supervised learning for speech enhancement and separation—0
S2F2: Self-Supervised High Fidelity Face Reconstruction from Monocular Image—0
One Network Doesn't Rule Them All: Moving Beyond Handcrafted Architectures in Self-Supervised Learning—0
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities—0
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training—0
Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases—0
Towards Self-Supervised Learning of Global and Object-Centric RepresentationsCode0
SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning—0
Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification—0
Active Self-Semi-Supervised Learning for Few Labeled Samples—0
Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learning—0
CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification—0
Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation—0
Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging dataCode0
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervisionCode0
Measuring Self-Supervised Representation Quality for Downstream Classification using Discriminative Features—0
A study on the distribution of social biases in self-supervised learning visual models—0
Audio Self-supervised Learning: A Survey—0
RestainNet: a self-supervised digital re-stainer for stain normalization—0
Understanding Contrastive Learning Requires Incorporating Inductive Biases—0
SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification—0
Adversarial Contrastive Self-Supervised Learning—0
A Simple Self-Supervised ECG Representation Learning Method via Manipulated Temporal-Spatial Reverse Detection—0
Refining Self-Supervised Learning in Imaging: Beyond Linear Metric—0
A Survey of Multilingual Models for Automatic Speech Recognition—0
Fully Self-Supervised Learning for Semantic Segmentation—0
PASSL—0
Deep learning based domain adaptation for mitochondria segmentation on EM volumesCode0
Assessing the State of Self-Supervised Human Activity Recognition using Wearables—0
Tripartite: Tackle Noisy Labels by a More Precise Partition—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