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 4751–4800 of 5044 papers

TitleStatusHype
Self-supervised Learning: Generative or Contrastive—0
Bootstrap your own latent: A new approach to self-supervised LearningCode1
DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition—0
Adversarial Self-Supervised Contrastive LearningCode1
Longitudinal Self-Supervised Learning—0
Video Understanding as Machine Translation—0
MatchGAN: A Self-Supervised Semi-Supervised Conditional Generative Adversarial Network—0
Self-Supervised Relational Reasoning for Representation LearningCode1
Self-Supervised Learning Aided Class-Incremental Lifelong Learning—0
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning—0
Self-Supervised Reinforcement Learning for Recommender Systems—0
Self-supervised Learning from a Multi-view PerspectiveCode1
Contrastive Multi-View Representation Learning on GraphsCode1
Few-Shot Generative Conversational Query RewritingCode1
Object Segmentation Without Labels with Large-Scale Generative ModelsCode1
Speaker Diarization as a Fully Online Learning Problem in MiniVoxCode1
Unsupervised Transfer Learning with Self-Supervised Remedy—0
3D Self-Supervised Methods for Medical ImagingCode1
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised LearningCode0
Auto-Rectify Network for Unsupervised Indoor Depth EstimationCode1
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning—0
Self-supervised Training of Graph Convolutional NetworksCode1
Self-Supervised Domain-Aware Generative Network for Generalized Zero-Shot Learning—0
Self2Self With Dropout: Learning Self-Supervised Denoising From Single ImageCode1
Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution—0
Graph-based, Self-Supervised Program Repair from Diagnostic FeedbackCode1
Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch GestaltCode1
Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio RepresentationCode0
CERT: Contrastive Self-supervised Learning for Language UnderstandingCode0
Self-Supervised Human Depth Estimation from Monocular VideosCode1
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid DecoderCode1
Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition?—0
Contrastive Self-Supervised Learning for Commonsense ReasoningCode1
Efficient and Information-Preserving Future Frame Prediction and BeyondCode1
Self-Supervised Learning of Appliance Usage—0
Seeing voices and hearing voices: learning discriminative embeddings using cross-modal self-supervision—0
UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP—0
Audio-Visual Instance Discrimination with Cross-Modal AgreementCode1
On the Role of Visual Cues in Audiovisual Speech Enhancement—0
Extending and Analyzing Self-Supervised Learning Across DomainsCode1
Self-supervised Learning for Astronomical Image Classification—0
Supervised Contrastive LearningCode2
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans—0
Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges—0
Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data—0
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes—0
Self6D: Self-Supervised Monocular 6D Object Pose EstimationCode1
Distilling Localization for Self-Supervised Representation Learning—0
Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning—0
Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences—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