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 3301–3350 of 5044 papers

TitleStatusHype
CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments—0
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning—0
Curriculum Learning Meets Weakly Supervised Modality Correlation Learning—0
Custom Object Detection via Multi-Camera Self-Supervised Learning—0
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions—0
CycleCL: Self-supervised Learning for Periodic Videos—0
D2SA: Dual-Stage Distribution and Slice Adaptation for Efficient Test-Time Adaptation in MRI Reconstruction—0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition—0
A Theoretical Characterization of Optimal Data Augmentations in Self-Supervised Learning—0
Data Collection-free Masked Video Modeling—0
Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech—0
Data-Efficient Contrastive Learning by Differentiable Hard Sample and Hard Positive Pair Generation—0
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling—0
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach—0
Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning—0
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning—0
Data Scarcity in Recommendation Systems: A Survey—0
DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner—0
DDOS: A MOS Prediction Framework utilizing Domain Adaptive Pre-training and Distribution of Opinion Scores—0
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning—0
Deciphering the Projection Head: Representation Evaluation Self-supervised Learning—0
Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification—0
Decoupled Self-supervised Learning for Non-Homophilous Graphs—0
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph—0
Combining Self-Supervised and Supervised Learning with Noisy Labels—0
DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders—0
Deep Active Ensemble Sampling For Image Classification—0
Deep Active Learning Using Barlow Twins—0
Deep Anomaly Detection in Text—0
Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems—0
Deep Augmentation: Self-Supervised Learning with Transformations in Activation Space—0
Deep Bregman Divergence for Contrastive Learning of Visual Representations—0
Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasets—0
Deep Clustering with Features from Self-Supervised Pretraining—0
Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation—0
DeepFIB: Self-Imputation for Time Series Anomaly Detection—0
DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model—0
Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation—0
Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods,Datasets,and Future Directions—0
Deep Learning Model Security: Threats and Defenses—0
Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions—0
Deep Metric Learning Assisted by Intra-variance in A Semi-supervised View of Learning—0
Deep Metric Learning with Spherical Embedding—0
Deep Pattern Network for Click-Through Rate Prediction—0
Deep Projective Rotation Estimation through Relative Supervision—0
Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection—0
DeepSet SimCLR: Self-supervised deep sets for improved pathology representation learning—0
Deep Spectro-temporal Artifacts for Detecting Synthesized Speech—0
Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models—0
Delving Deeper into Data Scaling in Masked Image Modeling—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