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 2201–2250 of 5044 papers

TitleStatusHype
Weakly-Supervised Speech Pre-training: A Case Study on Target Speech Recognition—0
Image as First-Order Norm+Linear Autoregression: Unveiling Mathematical Invariance—0
MPE4G: Multimodal Pretrained Encoder for Co-Speech Gesture Generation—0
Reverse Engineering Self-Supervised Learning—0
Spoofing Attacker Also Benefits from Self-Supervised Pretrained Model—0
Learning high-level visual representations from a child's perspective without strong inductive biasesCode1
Collaborative Auto-encoding for Blind Image Quality AssessmentCode0
Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss—0
Label-Efficient Learning in Agriculture: A Comprehensive ReviewCode1
Delving Deeper into Data Scaling in Masked Image Modeling—0
An Autoencoder-based Snow Drought Index—0
Masked Modeling Duo for Speech: Specializing General-Purpose Audio Representation to Speech using Denoising Distillation—0
Difference-Masking: Choosing What to Mask in Continued PretrainingCode0
TranUSR: Phoneme-to-word Transcoder Based Unified Speech Representation Learning for Cross-lingual Speech Recognition—0
Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?Code0
Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative TrainingCode5
Multi-behavior Self-supervised Learning for RecommendationCode1
Scaling Speech Technology to 1,000+ LanguagesCode1
Revisiting pre-trained remote sensing model benchmarks: resizing and normalization mattersCode1
EnSiam: Self-Supervised Learning With Ensemble Representations—0
ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer—0
Denoised Self-Augmented Learning for Social RecommendationCode1
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Self-supervised representations in speech-based depression detection—0
Pengi: An Audio Language Model for Audio TasksCode2
Phonetic and Prosody-aware Self-supervised Learning Approach for Non-native Fluency Scoring—0
S-JEA: Stacked Joint Embedding Architectures for Self-Supervised Visual Representation Learning—0
SurgMAE: Masked Autoencoders for Long Surgical Video Analysis—0
Equivariant Multi-Modality Image FusionCode2
Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking DistillationCode1
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature IndividualizationCode1
Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition—0
Zero-Shot Text Classification via Self-Supervised TuningCode1
DiffUTE: Universal Text Editing Diffusion ModelCode1
Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals—0
Adaptive Graph Contrastive Learning for RecommendationCode1
A benchmark for computational analysis of animal behavior, using animal-borne tagsCode1
Tuned Contrastive Learning—0
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes—0
Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant ClusteringCode1
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly DetectionCode0
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark—0
Speech Separation based on Contrastive Learning and Deep Modularization—0
Self-Supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET—0
HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph EmbeddingCode0
Rethinking Data Augmentation for Tabular Data in Deep LearningCode1
XAI for Self-supervised Clustering of Wireless Spectrum Activity—0
Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learningCode0
State Representation Learning Using an Unbalanced AtlasCode0
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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