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 3901–3950 of 5044 papers

TitleStatusHype
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
Investigation of Ensemble features of Self-Supervised Pretrained Models for Automatic Speech Recognition—0
Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain—0
IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G—0
Is Anisotropy Inherent to Transformers?—0
Is Dataset Quality Still a Concern in Diagnosis Using Large Foundation Model?—0
Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video—0
Is Lip Region-of-Interest Sufficient for Lipreading?—0
Is Multi-Task Learning an Upper Bound for Continual Learning?—0
Is Self-Supervised Learning More Robust Than Supervised Learning?—0
Is Self-Supervised Pre-training on Satellite Imagery Better than ImageNet? A Systematic Study with Sentinel-2—0
Is Tokenization Needed for Masked Particle Modelling?—0
IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction—0
JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture—0
JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks—0
J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume—0
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain—0
Joint Embedding of 2D and 3D Networks for Medical Image Anomaly Detection—0
Joint-Embedding Predictive Architecture for Self-Supervised Learning of Mask Classification Architecture—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