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 3951–4000 of 5044 papers

TitleStatusHype
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
Label-efficient Time Series Representation Learning: A Review—0
Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling—0
Label-free Monitoring of Self-Supervised Learning Progress—0
Label-free segmentation from cardiac ultrasound using self-supervised learning—0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
Ladder Siamese Network: a Method and Insights for Multi-level Self-Supervised Learning—0
LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space—0
Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection—0
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model—0
Language-based Action Concept Spaces Improve Video Self-Supervised Learning—0
Language Bias in Self-Supervised Learning For Automatic Speech Recognition—0
Language Models sounds the Death Knell of Knowledge Graphs—0
Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition—0
Laplacian Denoising Autoencoder—0
Large Cognition Model: Towards Pretrained EEG Foundation Model—0
Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents—0
Large-scale Foundation Models and Generative AI for BigData Neuroscience—0
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping—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