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 2101–2150 of 5044 papers

TitleStatusHype
Federated Contrastive Learning for Volumetric Medical Image Segmentation—0
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning—0
A Survey of Deep Learning: From Activations to Transformers—0
A Cookbook of Self-Supervised Learning—0
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging—0
Compact Speech Translation Models via Discrete Speech Units Pretraining—0
3-DUSSS: 3-Dimensional Ultrasonic Self Supervised Segmentation—0
A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation—0
Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition—0
A generic self-supervised learning (SSL) framework for representation learning from spectra-spatial feature of unlabeled remote sensing imagery—0
Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds—0
LocalGCL: Local-aware Contrastive Learning for Graphs—0
LPaintB: Learning to Paint from Self-Supervision—0
Masked Image Modeling Advances 3D Medical Image Analysis—0
MindSemantix: Deciphering Brain Visual Experiences with a Brain-Language Model—0
Feature Normalization for Fine-tuning Self-Supervised Models in Speech Enhancement—0
Feature Learning in Image Hierarchies using Functional Maximal Correlation—0
Feature diversity in self-supervised learning—0
Feature Correlation-guided Knowledge Transfer for Federated Self-supervised Learning—0
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation—0
Feature Alignment by Uncertainty and Self-Training for Source-Free Unsupervised Domain Adaptation—0
Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models—0
A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment—0
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features—0
FeaRLESS: Feature Refinement Loss for Ensembling Self-Supervised Learning Features in Robust End-to-end Speech Recognition—0
Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation—0
FaultSeg Swin-UNETR: Transformer-Based Self-Supervised Pretraining Model for Fault Recognition—0
Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning—0
A study on the distribution of social biases in self-supervised learning visual models—0
Fast Word Error Rate Estimation Using Self-Supervised Representations for Speech and Text—0
Fast Traversability Estimation for Wild Visual Navigation—0
FastInject: Injecting Unpaired Text Data into CTC-based ASR training—0
A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras—0
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning—0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation—0
Fast Adaptation for Human Pose Estimation via Meta-Optimization—0
Colour augmentation for improved semi-supervised semantic segmentation—0
A Study of the Generalizability of Self-Supervised Representations—0
Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives—0
Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning—0
A Study of Forward-Forward Algorithm for Self-Supervised Learning—0
Failure-Proof Non-Contrastive Self-Supervised Learning—0
FaceGPT: Self-supervised Learning to Chat about 3D Human Faces—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
A Generative Self-Supervised Framework using Functional Connectivity in fMRI Data—0
Leveraging Time Irreversibility with Order-Contrastive Pre-training—0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast—0
Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors—0
Astromer 2—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