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

Showing 45514600 of 5044 papers

TitleStatusHype
Knowledge-aware Contrastive Molecular Graph Learning0
Revisiting Self-Supervised Monocular Depth EstimationCode0
DIG: A Turnkey Library for Diving into Graph Deep Learning Research0
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations0
Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation0
Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomalies0
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI0
Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking0
Pretraining Neural Architecture Search Controllers with Locality-based Self-Supervised LearningCode0
Distributed Linear-Quadratic Control with Graph Neural Networks0
Self-Supervised Motion Retargeting with Safety Guarantee0
Self-supervised Text-to-SQL Learning with Header Alignment Training0
Cross-modal Image Retrieval with Deep Mutual Information Maximization0
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning0
Multi-Pretext Attention Network for Few-shot Learning with Self-supervisionCode0
Self-supervised Regularization for Text Classification0
Self-supervised SAR-optical Data Fusion and Land-cover Mapping using Sentinel-1/-2 Images0
Wav2vec-C: A Self-supervised Model for Speech Representation Learning0
Self-Supervision by Prediction for Object Discovery in Videos0
Bootstrapped Representation Learning on Graphs0
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations0
Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis0
Liver Fibrosis and NAS scoring from CT images using self-supervised learning and texture encodingCode0
Helicopter Track Identification with Autoencoder0
Self-supervised Pretraining of Visual Features in the Wild0
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations0
Contrastive Separative Coding for Self-supervised Representation Learning0
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation0
A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning0
Towards Continual, Online, Self-Supervised DepthCode0
Domain Adapting Ability of Self-Supervised Learning for Face Recognition0
Towards Robust Graph Contrastive Learning0
Enabling the Network to Surf the Internet0
"Train one, Classify one, Teach one" -- Cross-surgery transfer learning for surgical step recognition0
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation0
Self-Supervised Learning of Graph Neural Networks: A Unified Review0
UPRec: User-Aware Pre-training for Recommender Systems0
Contrastive Self-supervised Neural Architecture SearchCode0
Self-Supervised Learning via multi-Transformation Classification for Action Recognition0
An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation0
Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents0
MAPGN: MAsked Pointer-Generator Network for sequence-to-sequence pre-training0
Adversarial defense for automatic speaker verification by cascaded self-supervised learning models0
Self-Supervised Multisensor Change Detection0
Self-supervised learning for fast and scalable time series hyper-parameter tuning0
Improving Scene Graph Classification by Exploiting Knowledge from Texts0
Conversational Query Rewriting with Self-supervised Learning0
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning0
Custom Object Detection via Multi-Camera Self-Supervised Learning0
Multi-Task Self-Supervised Pre-Training for Music Classification0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pretraining: NoneImages & Text57.5Unverified
2Pretraining: ShEDImages & Text54.3Unverified
3Pretraining: e-MixImages & Text48.9Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy91.7Unverified
2ResNet18Accuracy91.02Unverified
3MV-MRAccuracy89.67Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy93.89Unverified
2ResNet18average top-1 classification accuracy92.58Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy72.51Unverified
2ResNet18average top-1 classification accuracy69.31Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy82.64Unverified
2CorInfomax (ResNet18)Top-1 Accuracy80.48Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy51.84Unverified
2ResNet18average top-1 classification accuracy51.67Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy93.18Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy71.61Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid BYOL-S/CvTAccuracy67.2Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy54.86Unverified