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 3851–3900 of 5044 papers

TitleStatusHype
Improving Lesion Segmentation in Medical Images by Global and Regional Feature Compensation—0
Improving Masked Autoencoders by Learning Where to Mask—0
Improving Node Representation by Boosting Target-Aware Contrastive Loss—0
Improving Object Detection with Selective Self-supervised Self-training—0
Improving out-of-distribution generalization via multi-task self-supervised pretraining—0
Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization—0
Improving self-supervised representation learning via sequential adversarial masking—0
Improving Sentence Representations with Consensus Maximisation—0
Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data—0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables—0
Improving Streaming Transformer Based ASR Under a Framework of Self-supervised Learning—0
Improving Transformer-based Sequential Recommenders through Preference Editing—0
Improving Ultrasound Tongue Image Reconstruction from Lip Images Using Self-supervised Learning and Attention Mechanism—0
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions—0
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight—0
IMU Based Deep Stride Length Estimation With Self-Supervised Learning—0
In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains—0
Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation—0
In-Context Symmetries: Self-Supervised Learning through Contextual World Models—0
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning—0
Incorporating Unlabelled Data into Bayesian Neural Networks—0
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis—0
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning—0
Incremental False Negative Detection for Contrastive Learning—0
Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection—0
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification—0
Inductive biases in deep learning models for weather prediction—0
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation—0
Infinite Width Limits of Self Supervised Neural Networks—0
InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity—0
InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals—0
InfoNCE is variational inference in a recognition parameterised model—0
Informal Safety Guarantees for Simulated Optimizers Through Extrapolation from Partial Simulations—0
Information-guided pixel augmentation for pixel-wise contrastive learning—0
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation—0
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields—0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding—0
Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding—0
In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers—0
Instance and Category Supervision are Alternate Learners for Continual Learning—0
Instance-aware Self-supervised Learning for Nuclei Segmentation—0
Instance Image Retrieval by Learning Purely From Within the Dataset—0
Integrating Auxiliary Information in Self-supervised Learning—0
Integrating Emotional and Linguistic Models for Ethical Compliance in Large Language 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