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
Shape Self-Correction for Unsupervised Point Cloud Understanding—0
Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning—0
Sharpness & Shift-Aware Self-Supervised Learning—0
Self-supervised SAR-optical Data Fusion and Land-cover Mapping using Sentinel-1/-2 Images—0
Shorter SPECT Scans Using Self-supervised Coordinate Learning to Synthesize Skipped Projection Views—0
Shot Contrastive Self-Supervised Learning for Scene Boundary Detection—0
Shot in the Dark: Few-Shot Learning with No Base-Class Labels—0
Should we pre-train a decoder in contrastive learning for dense prediction tasks?—0
Show from Tell: Audio-Visual Modelling in Clinical Settings—0
Shuffle & Divide: Contrastive Learning for Long Text—0
Shuffle to Learn: Self-supervised learning from permutations via differentiable ranking—0
Siamese Encoding and Alignment by Multiscale Learning with Self-Supervision—0
Siamese Networks with Soft Labels for Unsupervised Lesion Detection and Patch Pretraining on Screening Mammograms—0
Siamese Prototypical Contrastive Learning—0
Siamese Transformer Networks for Few-shot Image Classification—0
SIDME: Self-supervised Image Demoiréing via Masked Encoder-Decoder Reconstruction—0
SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding—0
SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition—0
Silence is Sweeter Than Speech: Self-Supervised Model Using Silence to Store Speaker Information—0
SimMER: Simple Maximization of Entropy and Rank for Self-supervised Representation Learning—0
SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images—0
Simple Contrastive Representation Adversarial Learning for NLP Tasks—0
Simple Control Baselines for Evaluating Transfer Learning—0
SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data—0
Simple Unsupervised Knowledge Distillation With Space Similarity—0
Auto-Linear Phenomenon in Subsurface Imaging—0
SimPLR: A Simple and Plain Transformer for Scaling-Efficient Object Detection and Segmentation—0
SimSiam Naming Game: A Unified Approach for Representation Learning and Emergent Communication—0
Simulating Personal Food Consumption Patterns using a Modified Markov Chain—0
Simultaneously Learning Robust Audio Embeddings and balanced Hash codes for Query-by-Example—0
Simultaneous or Sequential Training? How Speech Representations Cooperate in a Multi-Task Self-Supervised Learning System—0
Singing Voice Conversion with Accompaniment Using Self-Supervised Representation-Based Melody Features—0
Single-photon Image Super-resolution via Self-supervised Learning—0
SinSim: Sinkhorn-Regularized SimCLR—0
SIR: Self-supervised Image Rectification via Seeing the Same Scene from Multiple Different Lenses—0
Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar—0
S-JEA: Stacked Joint Embedding Architectures for Self-Supervised Visual Representation Learning—0
SkillMatch: Evaluating Self-supervised Learning of Skill Relatedness—0
SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learning—0
Skip-Attention: Improving Vision Transformers by Paying Less Attention—0
SKQVC: One-Shot Voice Conversion by K-Means Quantization with Self-Supervised Speech Representations—0
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images—0
SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging—0
Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps—0
SLiDE: Self-supervised LiDAR De-snowing through Reconstruction Difficulty—0
SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation—0
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification—0
SLYKLatent: A Learning Framework for Gaze Estimation Using Deep Facial Feature Learning—0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models—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