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 3451–3500 of 5044 papers

TitleStatusHype
Exploring the Effect of Primitives for Compositional Generalization in Vision-and-LanguageCode0
Tracking Passengers and Baggage Items using Multiple Overhead Cameras at Security CheckpointsCode0
Ponder: Point Cloud Pre-training via Neural Rendering—0
Deep Active Learning Using Barlow Twins—0
3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI—0
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning—0
S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning—0
Exploring Efficiency of Vision Transformers for Self-Supervised Monocular Depth EstimationCode0
Don't Be So Sure! Boosting ASR Decoding via Confidence Relaxation—0
GEDI: GEnerative and DIscriminative Training for Self-Supervised Learning—0
A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor NetworksCode0
Benchmark for Uncertainty & Robustness in Self-Supervised LearningCode0
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse—0
Local Policy Improvement for Recommender Systems—0
Understanding and Improving the Role of Projection Head in Self-Supervised Learning—0
Offline Clustering Approach to Self-supervised Learning for Class-imbalanced Image Data—0
Metadata-guided Consistency Learning for High Content ImagesCode0
Joint Embedding of 2D and 3D Networks for Medical Image Anomaly Detection—0
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction—0
Leveraging Pre-Trained Acoustic Feature Extractor For Affective Vocal Bursts TasksCode0
Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language Models—0
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning—0
COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images—0
Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling—0
DQnet: Cross-Model Detail Querying for Camouflaged Object Detection—0
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs—0
Improving self-supervised representation learning via sequential adversarial masking—0
Curriculum Learning Meets Weakly Supervised Modality Correlation Learning—0
Image Compression with Product Quantized Masked Image Modeling—0
Semantics-Consistent Feature Search for Self-Supervised Visual Representation Learning—0
OAMixer: Object-aware Mixing Layer for Vision TransformersCode0
CbwLoss: Constrained Bidirectional Weighted Loss for Self-supervised Learning of Depth and Pose—0
Z-SSMNet: Zonal-aware Self-supervised Mesh Network for Prostate Cancer Detection and Diagnosis with Bi-parametric MRICode0
TriNet: stabilizing self-supervised learning from complete or slow collapse on ASR—0
Accelerating Self-Supervised Learning via Efficient Training Strategies—0
Mitigating Spurious Correlations for Self-supervised RecommendationCode0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
NeRFEditor: Differentiable Style Decomposition for Full 3D Scene Editing—0
Progressive Multi-Scale Self-Supervised Learning for Speech Recognition—0
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit—0
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations—0
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning—0
MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning—0
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering—0
Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models—0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model—0
CHAPTER: Exploiting Convolutional Neural Network Adapters for Self-supervised Speech Models—0
ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation—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