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 3401–3450 of 5044 papers

TitleStatusHype
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking—0
Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing—0
Deciphering the Projection Head: Representation Evaluation Self-supervised Learning—0
Leveraging the Third Dimension in Contrastive Learning—0
Task-Agnostic Graph Neural Network Evaluation via Adversarial CollaborationCode0
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
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay—0
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive EstimationCode0
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
Human-Timescale Adaptation in an Open-Ended Task Space—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
Vision Learners Meet Web Image-Text Pairs—0
Gated Self-supervised Learning For Improving Supervised Learning—0
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
NarrowBERT: Accelerating Masked Language Model Pretraining and InferenceCode0
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis—0
Learning to Exploit Temporal Structure for Biomedical Vision-Language ProcessingCode0
Language Models sounds the Death Knell of Knowledge Graphs—0
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring—0
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching—0
REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service—0
MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology—0
Event Camera Data Pre-training—0
Skip-Attention: Improving Vision Transformers by Paying Less Attention—0
A New Perspective to Boost Vision Transformer for Medical Image Classification—0
STEPs: Self-Supervised Key Step Extraction and Localization from Unlabeled Procedural VideosCode0
Towards Effective Instance Discrimination Contrastive Loss for Unsupervised Domain AdaptationCode0
Self-supervised Pre-training for Mirror Detection—0
Spatio-Focal Bidirectional Disparity Estimation From a Dual-Pixel Image—0
SelfME: Self-Supervised Motion Learning for Micro-Expression Recognition—0
Contrastive Continuity on Augmentation Stability Rehearsal for Continual Self-Supervised Learning—0
Building3D: A Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds—0
Instance and Category Supervision are Alternate Learners for Continual Learning—0
Evolved Part Masking for Self-Supervised Learning—0
Contactless Pulse Estimation Leveraging Pseudo Labels and Self-Supervision—0
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning—0
ToThePoint: Efficient Contrastive Learning of 3D Point Clouds via RecyclingCode0
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space—0
HiVLP: Hierarchical Interactive Video-Language Pre-Training—0
Homeomorphism Alignment for Unsupervised Domain AdaptationCode0
Weakly Supervised Class-Agnostic Motion Prediction for Autonomous Driving—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