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
Trading robust representations for sample complexity through self-supervised visual experience—0
Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning—0
Trainable Class Prototypes for Few-Shot Learning—0
Training Articulatory Inversion Models for Interspeaker Consistency—0
Training Autoregressive Speech Recognition Models with Limited in-domain Supervision—0
Training Large ASR Encoders with Differential Privacy—0
Training Robust Zero-Shot Voice Conversion Models with Self-supervised Features—0
"Train one, Classify one, Teach one" -- Cross-surgery transfer learning for surgical step recognition—0
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections—0
TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation—0
Transfer Learning Application of Self-supervised Learning in ARPES—0
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms—0
Transferrable Contrastive Learning for Visual Domain Adaptation—0
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching—0
Transferring self-supervised pre-trained models for SHM data anomaly detection with scarce labeled data—0
Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training—0
Transformer-based Self-Supervised Fish Segmentation in Underwater Videos—0
Transformer-Based Self-Supervised Learning for Emotion Recognition—0
Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin—0
Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition—0
Transformer Meets Gated Residual Networks To Enhance Photoplethysmogram Artifact Detection Informed by Mutual Information Neural Estimation—0
Transformer models: an introduction and catalog—0
A Theoretical Analysis of Self-Supervised Learning for Vision Transformers—0
Transforming Heart Chamber Imaging: Self-Supervised Learning for Whole Heart Reconstruction and Segmentation—0
TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning—0
Transitive Invariance for Self-supervised Visual Representation Learning—0
TranUSR: Phoneme-to-word Transcoder Based Unified Speech Representation Learning for Cross-lingual Speech Recognition—0
TravelNet: Self-Supervised Physically Plausible Hand Motion Learning From Monocular Color Images—0
Triangular Contrastive Learning on Molecular Graphs—0
TriBYOL: Triplet BYOL for Self-Supervised Representation Learning—0
TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness—0
Virtual embeddings and self-consistency for self-supervised learning—0
TriNet: stabilizing self-supervised learning from complete or slow collapse on ASR—0
Tripartite: Tackle Noisy Labels by a More Precise Partition—0
Triplet Attention Transformer for Spatiotemporal Predictive Learning—0
TrojanDec: Data-free Detection of Trojan Inputs in Self-supervised Learning—0
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets—0
Out-of-distribution Partial Label Learning—0
Tuned Contrastive Learning—0
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data—0
Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality—0
Two-Stage Multi-task Self-Supervised Learning for Medical Image Segmentation—0
Two-Stage Representation Learning for Analyzing Movement Behavior Dynamics in People Living with Dementia—0
Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images—0
Two Stream Self-Supervised Learning for Action Recognition—0
UCM-VeID V2: A Richer Dataset and A Pre-training Method for UAV Cross-Modality Vehicle Re-Identification—0
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation—0
UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation—0
UFO2: A unified pre-training framework for online and offline speech recognition—0
UGMAE: A Unified Framework for Graph Masked Autoencoders—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