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 3601–3650 of 5044 papers

TitleStatusHype
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform LossCode1
SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained EncodersCode1
DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction—0
S2MS: Self-Supervised Learning Driven Multi-Spectral CT Image Enhancement—0
Link Prediction with Contextualized Self-Supervision—0
SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature VerificationCode1
Neural Manifold Clustering and EmbeddingCode1
Consistent 3D Hand Reconstruction in Video via self-supervised Learning—0
Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing—0
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning—0
Learning Pixel Trajectories with Multiscale Contrastive Random Walks—0
CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning—0
CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning—0
Revisiting Weakly Supervised Pre-Training of Visual Perception ModelsCode1
TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning—0
Self-Supervised Deep Blind Video Super-ResolutionCode1
Deep Cervix Model Development from Heterogeneous and Partially Labeled Image Datasets—0
BERT vs ALBERT explained—0
On The Effects of Learning Views on Neural Representations in Self-Supervised Learning—0
Knowledge Distillation as Self-Supervised Learning—0
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data—0
Video Transformers: A Survey—0
StolenEncoder: Stealing Pre-trained Encoders in Self-supervised LearningCode0
Semantic decoupled representation learning for remote sensing image change detection—0
SS-3DCapsNet: Self-supervised 3D Capsule Networks for Medical Segmentation on Less Labeled Data—0
Time Series Generation with Masked AutoencoderCode1
Boundary-aware Self-supervised Learning for Video Scene SegmentationCode1
STEdge: Self-training Edge Detection with Multi-layer Teaching and RegularizationCode0
Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?Code0
SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data—0
Maximizing Self-supervision from Thermal Image for Effective Self-supervised Learning of Depth and Ego-motionCode1
SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining—0
Bootstrapping Informative Graph Augmentation via A Meta Learning ApproachCode0
Reproducing BowNet: Learning Representations by Predicting Bags of Visual WordsCode0
Supervised Contrastive Learning for Recommendation—0
Cross-view Self-Supervised Learning on Heterogeneous Graph Neural Network via Bootstrapping—0
Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning—0
Self-Supervised Feature Learning from Partial Point Clouds via Pose Disentanglement—0
MGAE: Masked Autoencoders for Self-Supervised Learning on GraphsCode1
On the Effectiveness of Sampled Softmax Loss for Item Recommendation—0
Progressive Video Summarization via Multimodal Self-supervised LearningCode1
Self-Supervised Beat Tracking in Musical Signals with Polyphonic Contrastive Learning—0
Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough—0
Self-Supervised Approach to Addressing Zero-Shot Learning ProblemCode0
Self-supervised Learning from 100 Million Medical Images—0
Implicit Autoencoder for Point-Cloud Self-Supervised Representation LearningCode1
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
RigidFlow: Self-Supervised Scene Flow Learning on Point Clouds by Local Rigidity Prior—0
Exploiting Pseudo Labels in a Self-Supervised Learning Framework for Improved Monocular Depth Estimation—0
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency—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