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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4601–4650 of 5044 papers

TitleStatusHype
Results and findings of the 2021 Image Similarity Challenge—0
Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning—0
Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off—0
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation—0
Rethinking Self-Supervised Learning Within the Framework of Partial Information Decomposition—0
Rethinking supervised learning: insights from biological learning and from calling it by its name—0
Rethinking supervised learning: insights from biological learning and from calling it by its name—0
Rethinking the optimization process for self-supervised model-driven MRI reconstruction—0
Rethinking Weak-to-Strong Augmentation in Source-Free Domain Adaptive Object Detection—0
Retrieval-based Knowledge Augmented Vision Language Pre-training—0
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
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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