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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2876–2900 of 5044 papers

TitleStatusHype
Improving Sentence Representations with Consensus Maximisation—0
Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data—0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables—0
Improving Streaming Transformer Based ASR Under a Framework of Self-supervised Learning—0
Improving Transformer-based Sequential Recommenders through Preference Editing—0
Improving Ultrasound Tongue Image Reconstruction from Lip Images Using Self-supervised Learning and Attention Mechanism—0
Improving Scene Graph Classification by Exploiting Knowledge from Texts—0
Improving Zero-shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions—0
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight—0
IMU Based Deep Stride Length Estimation With Self-Supervised Learning—0
In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains—0
Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation—0
In-Context Symmetries: Self-Supervised Learning through Contextual World Models—0
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning—0
Incorporating Unlabelled Data into Bayesian Neural Networks—0
Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis—0
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning—0
Incremental False Negative Detection for Contrastive Learning—0
Incremental Layer-wise Self-Supervised Learning for Efficient Speech Domain Adaptation On Device—0
Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors—0
In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection—0
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification—0
Inductive biases in deep learning models for weather prediction—0
Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation—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