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 3551–3600 of 5044 papers

TitleStatusHype
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
Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification—0
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction—0
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study—0
Exploring SSL Discrete Tokens for Multilingual ASR—0
Exploring StyleGAN Latent Space for Face Alignment with Limited Training Data—0
Exploring Temporal Granularity in Self-Supervised Video Representation Learning—0
Exploring the Common Principal Subspace of Deep Features in Neural Networks—0
Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task—0
Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages—0
Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation—0
Exploring the Potential of SSL Models for Sound Event Detection—0
Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders—0
Exploring Visual Pre-training for Robot Manipulation: Datasets, Models and Methods—0
Exploring WavLM on Speech Enhancement—0
Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods—0
Extending Momentum Contrast with Cross Similarity Consistency Regularization—0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast—0
FaceGPT: Self-supervised Learning to Chat about 3D Human Faces—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