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 2651–2700 of 5044 papers

TitleStatusHype
Local Policy Improvement for Recommender Systems—0
Metadata-guided Consistency Learning for High Content ImagesCode0
Understanding and Improving the Role of Projection Head in Self-Supervised Learning—0
Leveraging Pre-Trained Acoustic Feature Extractor For Affective Vocal Bursts TasksCode0
Joint Embedding of 2D and 3D Networks for Medical Image Anomaly Detection—0
Similarity Contrastive Estimation for Image and Video Soft Contrastive Self-Supervised LearningCode1
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction—0
Deep Unfolded Tensor Robust PCA with Self-supervised LearningCode1
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning—0
Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language Models—0
COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images—0
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised LearningCode1
Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling—0
BEATs: Audio Pre-Training with Acoustic TokenizersCode1
DQnet: Cross-Model Detail Querying for Camouflaged Object Detection—0
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs—0
Improving self-supervised representation learning via sequential adversarial masking—0
Curriculum Learning Meets Weakly Supervised Modality Correlation Learning—0
Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and LanguageCode1
Image Compression with Product Quantized Masked Image Modeling—0
OAMixer: Object-aware Mixing Layer for Vision TransformersCode0
Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning—0
TriNet: stabilizing self-supervised learning from complete or slow collapse on ASR—0
CbwLoss: Constrained Bidirectional Weighted Loss for Self-supervised Learning of Depth and Pose—0
Z-SSMNet: Zonal-aware Self-supervised Mesh Network for Prostate Cancer Detection and Diagnosis with Bi-parametric MRICode0
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving ScenariosCode1
Transductive Linear Probing: A Novel Framework for Few-Shot Node ClassificationCode1
Accelerating Self-Supervised Learning via Efficient Training Strategies—0
Weakly Supervised Semantic Segmentation for Large-Scale Point CloudCode1
Benchmarking Self-Supervised Learning on Diverse Pathology DatasetsCode1
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
Mitigating Spurious Correlations for Self-supervised RecommendationCode0
Self-Supervised PPG Representation Learning Shows High Inter-Subject VariabilityCode1
Progressive Multi-Scale Self-Supervised Learning for Speech Recognition—0
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit—0
Spatio-Temporal Self-Supervised Learning for Traffic Flow PredictionCode2
NeRFEditor: Differentiable Style Decomposition for Full 3D Scene Editing—0
Giga-SSL: Self-Supervised Learning for Gigapixel ImagesCode1
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning—0
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations—0
MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning—0
Canonical Fields: Self-Supervised Learning of Pose-Canonicalized Neural FieldsCode1
SceneRF: Self-Supervised Monocular 3D Scene Reconstruction with Radiance FieldsCode2
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering—0
Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models—0
Exploring Stochastic Autoregressive Image Modeling for Visual RepresentationCode1
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image SynthesisCode1
DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model—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