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 1651–1700 of 5044 papers

TitleStatusHype
Teaching Robots to Build Simulations of Themselves—0
Benchmarking Pathology Feature Extractors for Whole Slide Image ClassificationCode1
Event Camera Data Dense Pre-training—0
Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain SolutionCode1
Large Pre-trained time series models for cross-domain Time series analysis tasksCode1
Self-Supervised Versus Supervised Training for Segmentation of Organoid Images—0
Self-Distilled Representation Learning for Time Series—0
ShapeMatcher: Self-Supervised Joint Shape Canonicalization, Segmentation, Retrieval and DeformationCode1
Lesion Search with Self-supervised Learning—0
Point Cloud Self-supervised Learning via 3D to Multi-view Masked AutoencoderCode1
LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement—0
Reprogramming Self-supervised Learning-based Speech Representations for Speaker Anonymization—0
Self-trained Panoptic Segmentation—0
Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and ClassificationCode0
PixT3: Pixel-based Table-To-Text GenerationCode0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
Self-supervised learning of multi-omics embeddings in the low-label, high-data regime—0
Cross-view and Cross-pose Completion for 3D Human Understanding—0
Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniquesCode1
PEMS: Pre-trained Epidemic Time-series Models—0
Dual-channel Prototype Network for few-shot Classification of Pathological Images—0
Mobility-Induced Graph Learning for WiFi Positioning—0
Rotation-Agnostic Image Representation Learning for Digital Pathology—0
On the Effectiveness of ASR Representations in Real-world Noisy Speech Emotion Recognition—0
Osteoporosis Prediction from Hand and Wrist X-rays using Image Segmentation and Self-Supervised Learning—0
PuzzleTuning: Explicitly Bridge Pathological and Natural Image with PuzzlesCode1
PECoP: Parameter Efficient Continual Pretraining for Action Quality AssessmentCode0
SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial DefectsCode1
Automatized Self-Supervised Learning for Skin Lesion Screening—0
Protein-ligand binding representation learning from fine-grained interactions—0
High-Performance Transformers for Table Structure Recognition Need Early ConvolutionsCode2
SS-MAE: Spatial-Spectral Masked Auto-Encoder for Multi-Source Remote Sensing Image ClassificationCode1
Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box ReconstructionCode0
Are foundation models efficient for medical image segmentation?—0
Image Generation and Learning Strategy for Deep Document Forgery Detection—0
Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning—0
LISBET: a machine learning model for the automatic segmentation of social behavior motifsCode1
PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning—0
Self-MI: Efficient Multimodal Fusion via Self-Supervised Multi-Task Learning with Auxiliary Mutual Information Maximization—0
A Foundation Model for Music InformaticsCode2
CycleCL: Self-supervised Learning for Periodic Videos—0
Multi-channel learning for integrating structural hierarchies into context-dependent molecular representationCode1
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection—0
Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells—0
TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model—0
Learning Time-Invariant Representations for Individual Neurons from Population DynamicsCode1
A Simple and Efficient Baseline for Data Attribution on ImagesCode1
Combating Bilateral Edge Noise for Robust Link PredictionCode1
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification—0
Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations—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