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 2301–2350 of 5044 papers

TitleStatusHype
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
Instance Image Retrieval by Learning Purely From Within the Dataset—0
Instance-aware Self-supervised Learning for Nuclei Segmentation—0
Instance and Category Supervision are Alternate Learners for Continual Learning—0
In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers—0
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning—0
Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding—0
Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding—0
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning—0
INoD: Injected Noise Discriminator for Self-Supervised Representation Learning in Agricultural Fields—0
DDOS: A MOS Prediction Framework utilizing Domain Adaptive Pre-training and Distribution of Opinion Scores—0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation—0
DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner—0
Information-guided pixel augmentation for pixel-wise contrastive learning—0
Data Scarcity in Recommendation Systems: A Survey—0
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning—0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
Informal Safety Guarantees for Simulated Optimizers Through Extrapolation from Partial Simulations—0
InfoNCE is variational inference in a recognition parameterised model—0
InfoMAE: Pair-Efficient Cross-Modal Alignment for Multimodal Time-Series Sensing Signals—0
InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity—0
Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning—0
Infinite Width Limits of Self Supervised Neural Networks—0
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach—0
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation—0
Inductive biases in deep learning models for weather prediction—0
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification—0
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling—0
In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device—0
Data-Efficient Contrastive Learning by Differentiable Hard Sample and Hard Positive Pair Generation—0
Balanced Deep CCA for Bird Vocalization Detection—0
Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation—0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning—0
Incremental False Negative Detection for Contrastive Learning—0
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning—0
Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech—0
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis—0
Incorporating Unlabelled Data into Bayesian Neural Networks—0
Knowledge Distillation for Human Action Anticipation—0
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning—0
Data Collection-free Masked Video Modeling—0
In-Context Symmetries: Self-Supervised Learning through Contextual World Models—0
Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing—0
In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains—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