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 1151–1200 of 5044 papers

TitleStatusHype
Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning MethodCode0
AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion EncodingCode5
A self-supervised text-vision framework for automated brain abnormality detection—0
JOSENet: A Joint Stream Embedding Network for Violence Detection in Surveillance VideosCode0
Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed ObservationsCode2
TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer—0
Adapting Self-Supervised Learning for Computational Pathology—0
On the Universality of Self-Supervised Learning—0
Investigating Self-Supervised Image Denoising with Denaturation—0
Self-Supervised Learning for Interventional Image Analytics: Towards Robust Device Trackers—0
Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning—0
Transformer-Based Self-Supervised Learning for Histopathological Classification of Ischemic Stroke Clot Origin—0
Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative AnalysisCode0
TFPred: Learning Discriminative Representations from Unlabeled Data for Few-Label Rotating Machinery Fault DiagnosisCode2
Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings—0
On Improving the Algorithm-, Model-, and Data- Efficiency of Self-Supervised Learning—0
MetaCoCo: A New Few-Shot Classification Benchmark with Spurious CorrelationCode0
SemiPL: A Semi-supervised Method for Event Sound Source LocalizationCode0
Self-supervised learning for classifying paranasal anomalies in the maxillary sinusCode0
MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion RecognitionCode1
Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation—0
SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological SignalsCode1
Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces—0
Self-supervised visual learning in the low-data regime: a comparative evaluation—0
HYPE: Hyperbolic Entailment Filtering for Underspecified Images and TextsCode1
Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection—0
Point-JEPA: A Joint Embedding Predictive Architecture for Self-Supervised Learning on Point CloudCode1
Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability, Composability, and Decomposability from Anatomy via Self-SupervisionCode1
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis—0
S2DEVFMAP: Self-Supervised Learning Framework with Dual Ensemble Voting Fusion for Maximizing Anomaly Prediction in Timeseries—0
Drawing the Line: Deep Segmentation for Extracting Art from Ancient Etruscan MirrorsCode0
Additive Margin in Contrastive Self-Supervised Frameworks to Learn Discriminative Speaker Representations—0
Non-Uniform Exposure Imaging via Neuromorphic Shutter Control—0
Vim4Path: Self-Supervised Vision Mamba for Histopathology ImagesCode2
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior—0
Text-dependent Speaker Verification (TdSV) Challenge 2024: Challenge Evaluation Plan—0
Self-Supervised Learning for User Localization—0
Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting—0
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data—0
Hypergraph Self-supervised Learning with Sampling-efficient SignalsCode0
Moving Object Segmentation: All You Need Is SAM (and Flow)Code3
OPTiML: Dense Semantic Invariance Using Optimal Transport for Self-Supervised Medical Image Representation—0
Pretraining Billion-scale Geospatial Foundational Models on Frontier—0
When are Foundation Models Effective? Understanding the Suitability for Pixel-Level Classification Using Multispectral Imagery—0
Deep Pattern Network for Click-Through Rate Prediction—0
Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition—0
Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models—0
Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition—0
Masked Autoencoders for Microscopy are Scalable Learners of Cellular BiologyCode2
Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression RecognitionCode1
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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