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 4101–4150 of 5044 papers

TitleStatusHype
Reverse Engineering Self-Supervised Learning—0
Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations—0
Revisit Event Generation Model: Self-Supervised Learning of Event-to-Video Reconstruction with Implicit Neural Representations—0
Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations—0
Supervised Momentum Contrastive Learning for Few-Shot Classification—0
Revisiting Fine-Tuning Strategies for Self-supervised Medical Imaging Analysis—0
Revisiting LARS for Large Batch Training Generalization of Neural Networks—0
Revisiting MAE pre-training for 3D medical image segmentation—0
Revisiting Model Stitching to Compare Neural Representations—0
Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation—0
Revisiting Self-supervised Learning of Speech Representation from a Mutual Information Perspective—0
Revisiting Self-Training with Regularized Pseudo-Labeling for Tabular Data—0
Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings—0
USTEP: Spatio-Temporal Predictive Learning under A Unified View—0
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples—0
Revolutionizing Wireless Networks with Self-Supervised Learning: A Pathway to Intelligent Communications—0
Reward-Driven Interaction: Enhancing Proactive Dialogue Agents through User Satisfaction Prediction—0
RGI : Regularized Graph Infomax for self-supervised learning on graphs—0
RGMIM: Region-Guided Masked Image Modeling for Learning Meaningful Representations from X-Ray Images—0
RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy—0
RigidFlow: Self-Supervised Scene Flow Learning on Point Clouds by Local Rigidity Prior—0
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation—0
R.I.P.: A Simple Black-box Attack on Continual Test-time Adaptation—0
RIT Boston at SemEval-2022 Task 5: Multimedia Misogyny Detection By Using Coherent Visual and Language Features from CLIP Model and Data-centric AI Principle—0
RmGPT: Rotating Machinery Generative Pretrained Model—0
Robot Gaining Accurate Pouring Skills through Self-Supervised Learning and Generalization—0
Robot Localization and Navigation through Predictive Processing using LiDAR—0
Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables—0
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging—0
Robust Audio-Visual Instance Discrimination—0
Robust infrared small target detection using self-supervised and a contrario paradigms—0
Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-ray Fluoroscopy—0
Robust Self-Supervised Learning with Lie Groups—0
Robust Semi-Supervised Learning for Histopathology Images through Self-Supervision Guided Out-of-Distribution Scoring—0
Learning Generalized Visual Odometry Using Position-Aware Optical Flow and Geometric Bundle Adjustment—0
Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training—0
RoMedFormer: A Rotary-Embedding Transformer Foundation Model for 3D Genito-Pelvic Structure Segmentation in MRI and CT—0
Rotation-Agnostic Image Representation Learning for Digital Pathology—0
RRLFSOR: An Efficient Self-Supervised Learning Strategy of Graph Convolutional Networks—0
Rumor Detection with Self-supervised Learning on Texts and Social Graph—0
Run Away From your Teacher: a New Self-Supervised Approach Solving the Puzzle of BYOL—0
Run Away From your Teacher: Understanding BYOL by a Novel Self-Supervised Approach—0
S2DEVFMAP: Self-Supervised Learning Framework with Dual Ensemble Voting Fusion for Maximizing Anomaly Prediction in Timeseries—0
S2F2: Self-Supervised High Fidelity Face Reconstruction from Monocular Image—0
S2MS: Self-Supervised Learning Driven Multi-Spectral CT Image Enhancement—0
S2-Net: Self-supervision Guided Feature Representation Learning for Cross-Modality Images—0
S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning—0
S3Former: Self-supervised High-resolution Transformer for Solar PV Profiling—0
S^5Mars: Semi-Supervised Learning for Mars Semantic Segmentation—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—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