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 2951–3000 of 5044 papers

TitleStatusHype
Investigation of Ensemble features of Self-Supervised Pretrained Models for Automatic Speech Recognition—0
Invisible Backdoor Attacks Using Data Poisoning in the Frequency Domain—0
IQFM A Wireless Foundational Model for I/Q Streams in AI-Native 6G—0
Is Anisotropy Inherent to Transformers?—0
Is Dataset Quality Still a Concern in Diagnosis Using Large Foundation Model?—0
Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video—0
Is Lip Region-of-Interest Sufficient for Lipreading?—0
Is Multi-Task Learning an Upper Bound for Continual Learning?—0
Is Self-Supervised Learning More Robust Than Supervised Learning?—0
Is Self-Supervised Pre-training on Satellite Imagery Better than ImageNet? A Systematic Study with Sentinel-2—0
Is Tokenization Needed for Masked Particle Modelling?—0
IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction—0
JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture—0
JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks—0
J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume—0
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain—0
Joint Embedding of 2D and 3D Networks for Medical Image Anomaly Detection—0
Joint-Embedding Predictive Architecture for Self-Supervised Learning of Mask Classification Architecture—0
Joint Embedding Self-Supervised Learning in the Kernel Regime—0
Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning—0
Joint Encoder-Decoder Self-Supervised Pre-training for ASR—0
Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition—0
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies—0
Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training—0
JointMotion: Joint Self-Supervision for Joint Motion Prediction—0
Joint Prediction and Denoising for Large-scale Multilingual Self-supervised Learning—0
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering—0
Joint Self-Supervised Learning for Vision-based Reinforcement Learning—0
Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement—0
Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges—0
Joint Supervised and Self-supervised Learning for MRI Reconstruction—0
Jumpstarting Surgical Computer Vision—0
Just Noticeable Difference Modeling for Face Recognition System—0
k2SSL: A Faster and Better Framework for Self-Supervised Speech Representation Learning—0
KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder—0
KDD-LOAM: Jointly Learned Keypoint Detector and Descriptors Assisted LiDAR Odometry and Mapping—0
Keep Learning: Self-supervised Meta-learning for Learning from Inference—0
Knolling Bot: Learning Robotic Object Arrangement from Tidy Demonstrations—0
Knowledge-aware Contrastive Molecular Graph Learning—0
Knowledge Distillation as Self-Supervised Learning—0
Knowledge distillation from language model to acoustic model: a hierarchical multi-task learning approach—0
Knowledge Distillation from Multiple Foundation Models for End-to-End Speech Recognition—0
Knowledge-guided EEG Representation Learning—0
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology—0
Learning Visual Affordances with Target-Orientated Deep Q-Network to Grasp Objects by Harnessing Environmental Fixtures—0
Knowledge Prompts: Injecting World Knowledge into Language Models through Soft Prompts—0
Label Anchored Contrastive Learning for Language Understanding—0
Label-Efficient 3D Brain Segmentation via Complementary 2D Diffusion Models with Orthogonal Views—0
Label-efficient audio classification through multitask learning and self-supervision—0
Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Contrastive Learning—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