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 3151–3200 of 5044 papers

TitleStatusHype
CASSL: Curriculum Accelerated Self-Supervised Learning—0
CA-SSLR: Condition-Aware Self-Supervised Learning Representation for Generalized Speech Processing—0
CASTing Your Model: Learning to Localize Improves Self-Supervised Representations—0
Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data—0
Causality and "In-the-Wild" Video-Based Person Re-ID: A Survey—0
Causal Speech Enhancement with Predicting Semantics based on Quantized Self-supervised Learning Features—0
CBF-AFA: Chunk-Based Multi-SSL Fusion for Automatic Fluency Assessment—0
CbwLoss: Constrained Bidirectional Weighted Loss for Self-supervised Learning of Depth and Pose—0
CEIR: Concept-based Explainable Image Representation Learning—0
CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning—0
Cell-ontology guided transcriptome foundation model—0
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention—0
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data—0
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data—0
Challenging Gradient Boosted Decision Trees with Tabular Transformers for Fraud Detection at Booking.com—0
Channel Self-Supervision for Online Knowledge Distillation—0
Chaos is a Ladder: A New Understanding of Contrastive Learning—0
CHAPTER: Exploiting Convolutional Neural Network Adapters for Self-supervised Speech Models—0
Characterizing the adversarial vulnerability of speech self-supervised learning—0
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction—0
CLAR: Contrastive Learning of Auditory Representations—0
Class-Discriminative Attention Maps for Vision Transformers—0
Classification and Self-Supervised Regression of Arrhythmic ECG Signals Using Convolutional Neural Networks—0
Classification by Attention: Scene Graph Classification with Prior Knowledge—0
Classification of Microscopy Images of Breast Tissue: Region Duplication based Self-Supervision vs. Off-the Shelf Deep Representations—0
Classification of Seeds using Domain Randomization on Self-Supervised Learning Frameworks—0
Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learning—0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision—0
Contrastive Representation Disentanglement for Clustering—0
ClimateGS: Real-Time Climate Simulation with 3D Gaussian Style Transfer—0
LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening—0
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs—0
Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learning—0
Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping—0
Clustering-based Unsupervised Generative Relation Extraction—0
Clustering Egocentric Images in Passive Dietary Monitoring with Self-Supervised Learning—0
Clustering Properties of Self-Supervised Learning—0
CNC-Net: Self-Supervised Learning for CNC Machining Operations—0
Coarse Is Better? A New Pipeline Towards Self-Supervised Learning with Uncurated Images—0
CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning—0
Cocktail HuBERT: Generalized Self-Supervised Pre-training for Mixture and Single-Source Speech—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
Coherent, super resolved radar beamforming using self-supervised learning—0
COIN: Co-Cluster Infomax for Bipartite Graphs—0
Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation—0
Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning—0
Colour augmentation for improved semi-supervised semantic segmentation—0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning—0
Show:102550
← PrevPage 64 of 101Next →
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