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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3601–3650 of 5044 papers

TitleStatusHype
SS-VAERR: Self-Supervised Apparent Emotional Reaction Recognition from Video—0
MixMask: Revisiting Masking Strategy for Siamese ConvNetsCode0
A survey on Self Supervised learning approaches for improving Multimodal representation learning—0
Effective Targeted Attacks for Adversarial Self-Supervised Learning—0
HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material ClassificationCode0
Towards Efficient and Effective Self-Supervised Learning of Visual RepresentationsCode0
Automatic separation of laminar-turbulent flows on aircraft wings and stabilisers via adaptive attention butterfly networkCode0
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation—0
MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling—0
Test-Time Training for Graph Neural Networks—0
Self-Supervised Learning Through Efference CopiesCode0
SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning—0
Learning Self-Regularized Adversarial Views for Self-Supervised Vision TransformersCode0
Extracting speaker and emotion information from self-supervised speech models via channel-wise correlationsCode0
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning—0
Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images—0
The Hidden Uniform Cluster Prior in Self-Supervised Learning—0
Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery—0
LEAVES: Learning Views for Time-Series Data in Contrastive Learning—0
Self-Supervised Learning of Linear Precoders under Non-Linear PA Distortion for Energy-Efficient Massive MIMO Systems—0
On Compressing Sequences for Self-Supervised Speech Models—0
Self-supervised video pretraining yields robust and more human-aligned visual representations—0
CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping—0
Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification—0
Deep Spectro-temporal Artifacts for Detecting Synthesized Speech—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
Deep Active Ensemble Sampling For Image Classification—0
Self-supervised debiasing using low rank regularization—0
Label-free segmentation from cardiac ultrasound using self-supervised learning—0
Knowledge Prompts: Injecting World Knowledge into Language Models through Soft Prompts—0
Exploiting map information for self-supervised learning in motion forecasting—0
Unsupervised Domain Adaptive Fundus Image Segmentation with Few Labeled Source Data—0
Exploring Efficient-tuning Methods in Self-supervised Speech Models—0
Non-intrusive Load Monitoring based on Self-supervised Learning—0
Grow and Merge: A Unified Framework for Continuous Categories Discovery—0
Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps—0
Brief Introduction to Contrastive Learning Pretext Tasks for Visual Representation—0
Granularity-aware Adaptation for Image Retrieval over Multiple Tasks—0
Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine LearningCode0
SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstruction—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank—0
Clean self-supervised MRI reconstruction from noisy, sub-sampled training data with Robust SSDUCode0
A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras—0
MTSMAE: Masked Autoencoders for Multivariate Time-Series Forecasting—0
Backdoor Attacks in the Supply Chain of Masked Image Modeling—0
Self-omics: A Self-supervised Learning Framework for Multi-omics Cancer DataCode0
An attention-based backend allowing efficient fine-tuning of transformer models for speaker verificationCode0
Show:102550
← PrevPage 73 of 101Next →
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