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 2901–2950 of 5044 papers

TitleStatusHype
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
Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition—0
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation—0
Intent Detection and Discovery from User Logs via Deep Semi-Supervised Contrastive Clustering—0
Intent Disentanglement and Feature Self-supervision for Novel Recommendation—0
Interactive Feature Embedding for Infrared and Visible Image Fusion—0
Interactive Masked Image Modeling for Multimodal Object Detection in Remote Sensing—0
Interest-oriented Universal User Representation via Contrastive Learning—0
Intermediate Self-supervised Learning for Machine Translation Quality Estimation—0
Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation—0
Interpretable agent communication from scratch (with a generic visual processor emerging on the side)—0
Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data—0
Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Interventional Contrastive Learning with Meta Semantic Regularizer—0
Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems—0
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning—0
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals—0
Intrinsically Motivated Self-supervised Learning in Reinforcement Learning—0
Invariant Structure Learning for Better Generalization and Causal Explainability—0
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning—0
Investigating a Baseline Of Self Supervised Learning Towards Reducing Labeling Costs For Image Classification—0
Investigating Active-learning-based Training Data Selection for Speech Spoofing Countermeasure—0
Investigating Content-Aware Neural Text-To-Speech MOS Prediction Using Prosodic and Linguistic Features—0
Investigating Fine- and Coarse-grained Structural Correspondences Between Deep Neural Networks and Human Object Image Similarity Judgments Using Unsupervised Alignment—0
Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction—0
Investigating Power laws in Deep Representation Learning—0
Investigating self-supervised features for expressive, multilingual voice conversion—0
Investigating Self-Supervised Image Denoising with Denaturation—0
Investigating self-supervised learning for speech enhancement and separation—0
End-to-End Lyrics Recognition with Self-supervised Learning—0
Investigating Self-Supervised Methods for Label-Efficient Learning—0
Investigating the 'Autoencoder Behavior' in Speech Self-Supervised Models: a focus on HuBERT's Pretraining—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