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 2751–2800 of 5044 papers

TitleStatusHype
Self-Supervised Versus Supervised Training for Segmentation of Organoid Images—0
Self-Distilled Representation Learning for Time Series—0
Lesion Search with Self-supervised Learning—0
Self-trained Panoptic Segmentation—0
LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement—0
Reprogramming Self-supervised Learning-based Speech Representations for Speaker Anonymization—0
Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and ClassificationCode0
PixT3: Pixel-based Table-To-Text GenerationCode0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
Self-supervised learning of multi-omics embeddings in the low-label, high-data regime—0
Cross-view and Cross-pose Completion for 3D Human Understanding—0
Dual-channel Prototype Network for few-shot Classification of Pathological Images—0
Mobility-Induced Graph Learning for WiFi Positioning—0
PEMS: Pre-trained Epidemic Time-series Models—0
Rotation-Agnostic Image Representation Learning for Digital Pathology—0
On the Effectiveness of ASR Representations in Real-world Noisy Speech Emotion Recognition—0
Osteoporosis Prediction from Hand and Wrist X-rays using Image Segmentation and Self-Supervised Learning—0
PECoP: Parameter Efficient Continual Pretraining for Action Quality AssessmentCode0
Automatized Self-Supervised Learning for Skin Lesion Screening—0
Protein-ligand binding representation learning from fine-grained interactions—0
Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box ReconstructionCode0
Are foundation models efficient for medical image segmentation?—0
Self-MI: Efficient Multimodal Fusion via Self-Supervised Multi-Task Learning with Auxiliary Mutual Information Maximization—0
Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning—0
PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning—0
Image Generation and Learning Strategy for Deep Document Forgery Detection—0
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection—0
CycleCL: Self-supervised Learning for Periodic Videos—0
Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells—0
TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model—0
Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations—0
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification—0
MNN: Mixed Nearest-Neighbors for Self-Supervised LearningCode0
COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised LearningCode0
Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback—0
FetusMapV2: Enhanced Fetal Pose Estimation in 3D Ultrasound—0
Adversarial Bootstrapped Question Representation Learning for Knowledge TracingCode0
On Linear Separation Capacity of Self-Supervised Representation Learning—0
Triplet Attention Transformer for Spatiotemporal Predictive Learning—0
Self-Supervised Multi-Modality Learning for Multi-Label Skin Lesion ClassificationCode0
SSL Framework for Causal Inconsistency between Structures and Representations—0
Patch-Wise Self-Supervised Visual Representation Learning: A Fine-Grained ApproachCode0
Large-scale Foundation Models and Generative AI for BigData Neuroscience—0
Unveiling the Potential of Probabilistic Embeddings in Self-Supervised Learning—0
FaultSeg Swin-UNETR: Transformer-Based Self-Supervised Pretraining Model for Fault Recognition—0
Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning—0
Towards Matching Phones and Speech Representations—0
Weakly-Supervised Surgical Phase Recognition—0
Show from Tell: Audio-Visual Modelling in Clinical Settings—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—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