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 3801–3850 of 5044 papers

TitleStatusHype
Towards A Universal Graph Structural Encoder—0
Towards Automatic Assessment of Self-Supervised Speech Models using Rank—0
Towards Better Spherical Sliced-Wasserstein Distance Learning with Data-Adaptive Discriminative Projection Direction—0
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning—0
Towards Computation- and Communication-efficient Computational Pathology—0
Towards Domain-Agnostic Contrastive Learning—0
Towards Early Prediction of Self-Supervised Speech Model Performance—0
Towards Effective Top-N Hamming Search via Bipartite Graph Contrastive Hashing—0
Towards evolution of Deep Neural Networks through contrastive Self-Supervised learning—0
Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout—0
Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach—0
Towards Fully Self-Supervised Learning of Knowledge from Unstructured Text—0
Towards Graph Contrastive Learning: A Survey and Beyond—0
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming—0
Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracing—0
Towards Intelligent Design: A Self-driven Framework for Collocated Clothing Synthesis Leveraging Fashion Styles and Textures—0
Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis—0
Towards Matching Phones and Speech Representations—0
Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits—0
Towards Robust Graph Contrastive Learning—0
Towards Robust Overlapping Speech Detection: A Speaker-Aware Progressive Approach Using WavLM—0
Towards Robust Speech Representation Learning for Thousands of Languages—0
Towards Safer Transportation: a self-supervised learning approach for traffic video deraining—0
Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data—0
Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning—0
Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning—0
Towards the Next Frontier in Speech Representation Learning Using Disentanglement—0
Towards the Sparseness of Projection Head in Self-Supervised Learning—0
Towards Unified Neural Decoding with Brain Functional Network Modeling—0
Towards Universal Speech Discrete Tokens: A Case Study for ASR and TTS—0
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer—0
Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing—0
Toward Super-Resolution for Appearance-Based Gaze Estimation—0
Towards Visual Ego-motion Learning in Robots—0
Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning—0
Trading robust representations for sample complexity through self-supervised visual experience—0
Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning—0
Trainable Class Prototypes for Few-Shot Learning—0
Training Articulatory Inversion Models for Interspeaker Consistency—0
Training Autoregressive Speech Recognition Models with Limited in-domain Supervision—0
Training Large ASR Encoders with Differential Privacy—0
Training Robust Zero-Shot Voice Conversion Models with Self-supervised Features—0
"Train one, Classify one, Teach one" -- Cross-surgery transfer learning for surgical step recognition—0
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections—0
TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation—0
Transfer Learning Application of Self-supervised Learning in ARPES—0
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms—0
Transferrable Contrastive Learning for Visual Domain Adaptation—0
Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching—0
Transferring self-supervised pre-trained models for SHM data anomaly detection with scarce labeled data—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