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 2851–2900 of 5044 papers

TitleStatusHype
AdaDim: Dimensionality Adaptation for SSL Representational Dynamics—0
Adapting Pre-trained 3D Models for Point Cloud Video Understanding via Cross-frame Spatio-temporal Perception—0
Adapting Self-Supervised Learning for Computational Pathology—0
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings—0
Adaptive Crowdsourcing Via Self-Supervised Learning—0
Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation—0
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
Adaptive Multi-layer Contrastive Graph Neural Networks—0
Dataset Awareness is not Enough: Implementing Sample-level Tail Encouragement in Long-tailed Self-supervised Learning—0
Automatic Self-supervised Learning for Social Recommendations—0
Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization—0
A dataset-free approach for self-supervised learning of 3D reflectional symmetries—0
Additional Positive Enables Better Representation Learning for Medical Images—0
Additive Logistic Mechanism for Privacy-Preserving Self-Supervised Learning—0
Additive Margin in Contrastive Self-Supervised Frameworks to Learn Discriminative Speaker Representations—0
Addressing Cold Start Problem for End-to-end Automatic Speech Scoring—0
A degree of image identification at sub-human scales could be possible with more advanced clusters—0
A dual task learning approach to fine-tune a multilingual semantic speech encoder for Spoken Language Understanding—0
Advanced Clustering Framework for Semiconductor Image Analytics Integrating Deep TDA with Self-Supervised and Transfer Learning Techniques—0
Advances and Challenges in Meta-Learning: A Technical Review—0
Advances in Self-Supervised Learning for Synthetic Aperture Sonar Data Processing, Classification, and Pattern Recognition—0
Advancing Comprehensive Aesthetic Insight with Multi-Scale Text-Guided Self-Supervised Learning—0
Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning—0
Adversarial Contrastive Self-Supervised Learning—0
Graph Contrastive Learning with Cross-view Reconstruction—0
Adversarial defense for automatic speaker verification by cascaded self-supervised learning models—0
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning—0
Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions—0
Adversarial Robustness of Discriminative Self-Supervised Learning in Vision—0
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition—0
Adversarial Semi-Supervised Multi-Domain Tracking—0
Adversarial Speaker Disentanglement Using Unannotated External Data for Self-supervised Representation Based Voice Conversion—0
Adversarial Transfer of Pose Estimation Regression—0
AETv2: AutoEncoding Transformations for Self-Supervised Representation Learning by Minimizing Geodesic Distances in Lie Groups—0
Affine transformation estimation improves visual self-supervised learning—0
A Foundational Brain Dynamics Model via Stochastic Optimal Control—0
Generalized Supervised Contrastive Learning—0
A Visual Representation-guided Framework with Global Affinity for Weakly Supervised Salient Object Detection—0
A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data—0
A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras—0
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features—0
A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery—0
Liquidity takers behavior representation through a contrastive learning approach—0
Aggregative Self-Supervised Feature Learning from a Limited Sample—0
AggSS: An Aggregated Self-Supervised Approach for Class-Incremental Learning—0
AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging—0
A Global Model Approach to Robust Few-Shot SAR Automatic Target Recognition—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
A Human Ear Reconstruction Autoencoder—0
A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications—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