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 2551–2600 of 5044 papers

TitleStatusHype
Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification—0
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial DefenseCode0
The unreasonable effectiveness of few-shot learning for machine translation—0
Hyperbolic Contrastive Learning—0
HaMuCo: Hand Pose Estimation via Multiview Collaborative Self-Supervised LearningCode1
Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms—0
Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach—0
Towards Label-Efficient Incremental Learning: A SurveyCode0
A Survey of Deep Learning: From Activations to Transformers—0
PointSmile: Point Self-supervised Learning via Curriculum Mutual Information—0
Exploring Image Augmentations for Siamese Representation Learning with Chest X-RaysCode1
Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing—0
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking—0
Deciphering the Projection Head: Representation Evaluation Self-supervised Learning—0
Mutual Wasserstein Discrepancy Minimization for Sequential RecommendationCode1
Task-Agnostic Graph Neural Network Evaluation via Adversarial CollaborationCode0
Cross-Architectural Positive Pairs improve the effectiveness of Self-Supervised LearningCode1
Leveraging the Third Dimension in Contrastive Learning—0
Navigating the Pitfalls of Active Learning Evaluation: A Systematic Framework for Meaningful Performance AssessmentCode1
STERLING: Synergistic Representation Learning on Bipartite Graphs—0
Self-Supervised Curricular Deep Learning for Chest X-Ray Image Classification—0
Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars—0
ClimaX: A foundation model for weather and climateCode2
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive EstimationCode0
Lexi: Self-Supervised Learning of the UI LanguageCode1
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Zorro: the masked multimodal transformerCode0
Unifying Synergies between Self-supervised Learning and Dynamic ComputationCode0
Slice Transformer and Self-supervised Learning for 6DoF Localization in 3D Point Cloud Maps—0
Blacks is to Anger as Whites is to Joy? Understanding Latent Affective Bias in Large Pre-trained Neural Language ModelsCode0
Regeneration Learning: A Learning Paradigm for Data Generation—0
Spatial Steerability of GANs via Self-Supervision from Discriminator—0
Self-Supervised Learning for Data Scarcity in a Fatigue Damage Prognostic ProblemCode1
Multiview Compressive Coding for 3D ReconstructionCode2
Self-Supervised Learning from Images with a Joint-Embedding Predictive ArchitectureCode2
Human-Timescale Adaptation in an Open-Ended Task Space—0
ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image RepresentationsCode1
Vision Learners Meet Web Image-Text Pairs—0
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network—0
MooseNet: A Trainable Metric for Synthesized Speech with a PLDA ModuleCode0
Gated Self-supervised Learning For Improving Supervised Learning—0
A Survey on Self-supervised Learning: Algorithms, Applications, and Future TrendsCode1
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIPCode1
Unsupervised Driving Event Discovery Based on Vehicle CAN-data—0
Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson's Disease—0
Learning to Exploit Temporal Structure for Biomedical Vision-Language ProcessingCode0
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis—0
NarrowBERT: Accelerating Masked Language Model Pretraining and InferenceCode0
Language Models sounds the Death Knell of Knowledge Graphs—0
Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data ClassificationCode1
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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