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 1801–1850 of 5044 papers

TitleStatusHype
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models—0
GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes—0
A theoretically grounded characterization of feature representations—0
AI can evolve without labels: self-evolving vision transformer for chest X-ray diagnosis through knowledge distillation—0
Hierarchical Cross Contrastive Learning of Visual Representations—0
Joint-Embedding Masked Autoencoder for Self-supervised Learning of Dynamic Functional Connectivity from the Human Brain—0
Hierarchical Metadata Information Constrained Self-Supervised Learning for Anomalous Sound Detection Under Domain Shift—0
CooPre: Cooperative Pretraining for V2X Cooperative Perception—0
Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation—0
Joint-Embedding Predictive Architecture for Self-Supervised Learning of Mask Classification Architecture—0
GAIA: A Foundation Model for Operational Atmospheric Dynamics—0
A theoretical framework for self-supervised contrastive learning for continuous dependent data—0
Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report—0
FUSSL: Fuzzy Uncertain Self Supervised Learning—0
Contextualized and Generalized Sentence Representations by Contrastive Self-Supervised Learning: A Case Study on Discourse Relation Analysis—0
A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—0
Fusion of stereo and still monocular depth estimates in a self-supervised learning context—0
Self-Supervised Deep Learning on Point Clouds by Reconstructing Space—0
Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes—0
Automated data curation for self-supervised learning in underwater acoustic analysis—0
Fusion from Decomposition: A Self-Supervised Approach for Image Fusion and Beyond—0
A Tale of Color Variants: Representation and Self-Supervised Learning in Fashion E-Commerce—0
Hodge-Aware Contrastive Learning—0
JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks—0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
HOME: High-Order Mixed-Moment-based Embedding for Representation Learning—0
Asymmetric Clean Segments-Guided Self-Supervised Learning for Robust Speaker Verification—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
Hopfield model with planted patterns: a teacher-student self-supervised learning model—0
Context-Aware Self-Supervised Learning of Whole Slide Images—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
Fully Unsupervised Annotation of C. Elegans—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
CPT-V: A Contrastive Approach to Post-Training Quantization of Vision Transformers—0
Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network—0
Fully Self-Supervised Learning for Semantic Segmentation—0
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning—0
How Robust is Unsupervised Representation Learning to Distribution Shift?—0
Fully neuromorphic vision and control for autonomous drone flight—0
Action Shuffle Alternating Learning for Unsupervised Action Segmentation—0
JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture—0
J-Invariant Volume Shuffle for Self-Supervised Cryo-Electron Tomogram Denoising on Single Noisy Volume—0
Joint Embedding Self-Supervised Learning in the Kernel Regime—0
Full waveform inversion with CNN-based velocity representation extension—0
How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario—0
A Human Ear Reconstruction Autoencoder—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