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 4701–4750 of 5044 papers

TitleStatusHype
Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images—0
SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learning—0
SelfME: Self-Supervised Motion Learning for Micro-Expression Recognition—0
Self-MI: Efficient Multimodal Fusion via Self-Supervised Multi-Task Learning with Auxiliary Mutual Information Maximization—0
Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk—0
SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay—0
Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction—0
Self Semi Supervised Neural Architecture Search for Semantic Segmentation—0
Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis—0
Self-Supervised 3D Monocular Object Detection by Recycling Bounding Boxes—0
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity—0
Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study—0
Self-supervised Adversarial Robustness for the Low-label, High-data Regime—0
Self-Supervised Alignment Learning for Medical Image Segmentation—0
Self-Supervised and Few-Shot Learning for Robust Bioaerosol Monitoring—0
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations—0
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook—0
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods—0
Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition—0
Self-supervised Auxiliary Learning for Texture and Model-based Hybrid Robust and Fair Featuring in Face Analysis—0
Self-supervised Auxiliary Loss for Metric Learning in Music Similarity-based Retrieval and Auto-tagging—0
Self-Supervised Backbone Framework for Diverse Agricultural Vision Tasks—0
Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection—0
Self-Supervised Beat Tracking in Musical Signals with Polyphonic Contrastive Learning—0
Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets?—0
Self-supervised clarification question generation for ambiguous multi-turn conversation—0
Self-supervised classification of dynamic obstacles using the temporal information provided by videos—0
Self-supervised Cloth Reconstruction via Action-conditioned Cloth Tracking—0
Self-Supervised Contextual Bandits in Computer Vision—0
Self-Supervised Contextual Language Representation of Radiology Reports to Improve the Identification of Communication Urgency—0
Self-Supervised Continuous Control without Policy Gradient—0
Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation—0
Self-Supervised Contrastive Pre-Training for Multivariate Point Processes—0
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation—0
Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution—0
Self supervised convolutional kernel based handcrafted feature harmonization: Enhanced left ventricle hypertension disease phenotyping on echocardiography—0
Self-supervised cost of transport estimation for multimodal path planning—0
Self-Supervised Curricular Deep Learning for Chest X-Ray Image Classification—0
Curriculum learning for self-supervised speaker verification—0
Self-supervised debiasing using low rank regularization—0
Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography—0
Self-supervised Dense 3D Reconstruction from Monocular Endoscopic Video—0
Self-Supervised Dialogue Learning—0
Self-supervised Dialogue Learning for Spoken Conversational Question Answering—0
Self-supervised Disentangled Representation Learning—0
Self-Supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images—0
Self-Supervised Domain-Aware Generative Network for Generalized Zero-Shot Learning—0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph—0
SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery—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