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 3751–3800 of 5044 papers

TitleStatusHype
The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery—0
The Counterattack of CNNs in Self-Supervised Learning: Larger Kernel Size might be All You Need—0
The effectiveness of unsupervised subword modeling with autoregressive and cross-lingual phone-aware networks—0
The Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound—0
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning—0
The Hidden Pitfalls of the Cosine Similarity Loss—0
The Hidden Uniform Cluster Prior in Self-Supervised Learning—0
The ID R&D VoxCeleb Speaker Recognition Challenge 2023 System Description—0
The Impact of Spatiotemporal Augmentations on Self-Supervised Audiovisual Representation Learning—0
The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning—0
The NT-Xent loss upper bound—0
The potential of self-supervised networks for random noise suppression in seismic data—0
The Power of Contrast for Feature Learning: A Theoretical Analysis—0
There is more to graphs than meets the eye: Learning universal features with self-supervision—0
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition—0
The role of audio-visual integration in the time course of phonetic encoding in self-supervised speech models—0
The Sparse Manifold Transform—0
The SSL Interplay: Augmentations, Inductive Bias, and Generalization—0
The THUEE System Description for the IARPA OpenASR21 Challenge—0
The Triad of Failure Modes and a Possible Way Out—0
The unreasonable effectiveness of few-shot learning for machine translation—0
The USTC-NERCSLIP Systems for The ICMC-ASR Challenge—0
The Vicomtech Spoofing-Aware Biometric System for the SASV Challenge—0
The ZevoMOS entry to VoiceMOS Challenge 2022—0
Thoughts on Objectives of Sparse and Hierarchical Masked Image Model—0
Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training—0
TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems—0
Advancing Human Action Recognition with Foundation Models trained on Unlabeled Public Videos—0
Time-based Self-supervised Learning for Wireless Capsule Endoscopy—0
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection—0
Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks—0
Time to augment self-supervised visual representation learning—0
TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer—0
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT—0
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation—0
T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data—0
To Balance or Not to Balance: A Simple-yet-Effective Approach for Learning with Long-Tailed Distributions—0
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection—0
To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review—0
Tone recognition in low-resource languages of North-East India: peeling the layers of SSL-based speech models—0
TOV: The Original Vision Model for Optical Remote Sensing Image Understanding via Self-supervised Learning—0
Toward Adaptive Categories: Dimensional Governance for Agentic AI—0
Toward a Foundation Model for Time Series Data—0
Toward a Geometrical Understanding of Self-supervised Contrastive Learning—0
Toward a realistic model of speech processing in the brain with self-supervised learning—0
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs—0
Toward Leveraging Pre-Trained Self-Supervised Frontends for Automatic Singing Voice Understanding Tasks: Three Case Studies—0
Towards a Generalizable Speech Marker for Parkinson's Disease Diagnosis—0
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations—0
MERaLiON-SpeechEncoder: Towards a Speech Foundation Model for Singapore and Beyond—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