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 2551–2600 of 5044 papers

TitleStatusHype
Evading Detection Actively: Toward Anti-Forensics against Forgery Localization—0
Evaluating Fairness in Self-supervised and Supervised Models for Sequential Data—0
Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for Robustness, Generalizability, and Multi-Domain Impact—0
Evaluating the fairness of fine-tuning strategies in self-supervised learning—0
Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery—0
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging—0
Evaluating Visual Explanations of Attention Maps for Transformer-based Medical Imaging—0
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment—0
Event Camera Data Dense Pre-training—0
Event Camera Data Pre-training—0
EventPoint: Self-Supervised Interest Point Detection and Description for Event-based Camera—0
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
Evolutionary algorithms meet self-supervised learning: a comprehensive survey—0
Evolutionary Augmentation Policy Optimization for Self-supervised Learning—0
Evolved Part Masking for Self-Supervised Learning—0
Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning—0
Exemplar Learning for Medical Image Segmentation—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning—0
Explaining, Analyzing, and Probing Representations of Self-Supervised Learning Models for Sensor-based Human Activity Recognition—0
Explicit homography estimation improves contrastive self-supervised learning—0
On the Universality of Self-Supervised Learning—0
Explicit Mutual Information Maximization for Self-Supervised Learning—0
Explicit Use of Topicality in Dialogue Response Generation—0
Exploiting Audio-Visual Consistency with Partial Supervision for Spatial Audio Generation—0
Exploiting Behavioral Consistence for Universal User Representation—0
Exploiting Data Hierarchy as a New Modality for Contrastive Learning—0
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study—0
Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation—0
Exploiting map information for self-supervised learning in motion forecasting—0
Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition—0
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation—0
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning—0
Antenna Response Consistency Driven Self-supervised Learning for WIFI-based Human Activity Recognition—0
Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models—0
Explorations in Self-Supervised Learning: Dataset Composition Testing for Object Classification—0
Exploring Balanced Feature Spaces for Representation Learning—0
Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery—0
Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition—0
Exploring Effective Mask Sampling Modeling for Neural Image Compression—0
Exploring Efficient-tuning Methods in Self-supervised Speech Models—0
Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding—0
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection—0
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects—0
Exploring Intrinsic Properties of Medical Images for Self-Supervised Binary Semantic Segmentation—0
Exploring learning environments for label\-efficient cancer diagnosis—0
Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling—0
Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection—0
Exploring Relations in Untrimmed Videos for Self-Supervised Learning—0
Exploring Self-Supervised Multi-view Contrastive Learning for Speech Emotion Recognition with Limited Annotations—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