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 3751–3800 of 5044 papers

TitleStatusHype
GUESS: Generative Uncertainty Ensemble for Self Supervision—0
Guided Diffusion from Self-Supervised Diffusion Features—0
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning—0
H^3GNNs: Harmonizing Heterophily and Homophily in GNNs via Joint Structural Node Encoding and Self-Supervised Learning—0
Hallucination Improves the Performance of Unsupervised Visual Representation Learning—0
HandMIM: Pose-Aware Self-Supervised Learning for 3D Hand Mesh Estimation—0
HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment—0
HASRD: Hierarchical Acoustic and Semantic Representation Disentanglement—0
HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation—0
HDR Imaging for Dynamic Scenes with Events—0
HealNet -- Self-Supervised Acute Wound Heal-Stage Classification—0
HeAR -- Health Acoustic Representations—0
Helicopter Track Identification with Autoencoder—0
HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer—0
HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative—0
Heterogeneous Space Fusion and Dual-Dimension Attention: A New Paradigm for Speech Enhancement—0
Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning—0
HFBRI-MAE: Handcrafted Feature Based Rotation-Invariant Masked Autoencoder for 3D Point Cloud Analysis—0
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
Hierarchical Contrastive Motion Learning for Video Action Recognition—0
Hierarchical Cross Contrastive Learning of Visual Representations—0
Hierarchical Metadata Information Constrained Self-Supervised Learning for Anomalous Sound Detection Under Domain Shift—0
Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation—0
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning—0
Higher-Order Spatial Information for Self-Supervised Place Cell Learning—0
Fusion Self-supervised Learning for Recommendation—0
High-Resolution Be Aware! Improving the Self-Supervised Real-World Super-Resolution—0
HiRes-FusedMIM: A High-Resolution RGB-DSM Pre-trained Model for Building-Level Remote Sensing Applications—0
HiVLP: Hierarchical Interactive Video-Language Pre-Training—0
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes—0
Hodge-Aware Contrastive Learning—0
HOME: High-Order Mixed-Moment-based Embedding for Representation Learning—0
Homomorphic Self-Supervised Learning—0
Hopfield model with planted patterns: a teacher-student self-supervised learning model—0
HoughCL: Finding Better Positive Pairs in Dense Self-supervised Learning—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
How Does SimSiam Avoid Collapse Without Negative Samples? Towards a Unified Understanding of Progress in SSL—0
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning—0
How Effective are Self-Supervised Models for Contact Identification in Videos—0
How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks—0
How Robust is Unsupervised Representation Learning to Distribution Shift?—0
How Self-Supervised Learning Can be Used for Fine-Grained Head Pose Estimation?—0
How Should We Extract Discrete Audio Tokens from Self-Supervised Models?—0
How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario—0
How to learn from unlabeled volume data: Self-Supervised 3D Context Feature Learning—0
How to Scale Your EMA—0
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems—0
How to Understand Masked Autoencoders—0
How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?—0
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot 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