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 3651–3700 of 5044 papers

TitleStatusHype
Fine-grained Anomaly Detection via Multi-task Self-Supervision—0
Fine-grained Multi-Modal Self-Supervised Learning—0
Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarization—0
FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving—0
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization—0
Flaky Performances when Pretraining on Relational Databases—0
FloorplanMAE:A self-supervised framework for complete floorplan generation from partial inputs—0
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy—0
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning—0
For One-Shot Decoding: Self-supervised Deep Learning-Based Polar Decoder—0
Foundational Models for Fault Diagnosis of Electrical Motors—0
Foundation Model for Whole-Heart Segmentation: Leveraging Student-Teacher Learning in Multi-Modal Medical Imaging—0
Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics—0
Foundation Models in Medical Imaging -- A Review and Outlook—0
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training—0
Frequency-Aware Self-Supervised Long-Tailed Learning—0
Friends and Foes in Learning from Noisy Labels—0
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
From Chaos to Clarity: 3DGS in the Dark—0
From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis—0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
From Prototypes to General Distributions: An Efficient Curriculum for Masked Image Modeling—0
Front-End Adapter: Adapting Front-End Input of Speech based Self-Supervised Learning for Speech Recognition—0
Frozen Language Model Helps ECG Zero-Shot Learning—0
Full waveform inversion with CNN-based velocity representation extension—0
Fully neuromorphic vision and control for autonomous drone flight—0
Fully Self-Supervised Learning for Semantic Segmentation—0
Fully Unsupervised Annotation of C. Elegans—0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
Fusion from Decomposition: A Self-Supervised Approach for Image Fusion and Beyond—0
Fusion of ECG Foundation Model Embeddings to Improve Early Detection of Acute Coronary Syndromes—0
Fusion of stereo and still monocular depth estimates in a self-supervised learning context—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—0
FUSSL: Fuzzy Uncertain Self Supervised Learning—0
Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report—0
GAIA: A Foundation Model for Operational Atmospheric Dynamics—0
GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes—0
Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models—0
Game State Learning via Game Scene Augmentation—0
GANORCON: Are Generative Models Useful for Few-shot Segmentation?—0
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition—0
Gated Self-supervised Learning For Improving Supervised Learning—0
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting—0
Gaussian Masked Autoencoders—0
Self-supervised learning of hologram reconstruction using physics consistency—0
GEDI: GEnerative and DIscriminative Training for Self-Supervised Learning—0
GelFlow: Self-supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
GenDistiller: Distilling Pre-trained Language Models based on an Autoregressive Generative Model—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