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 951–1000 of 5044 papers

TitleStatusHype
Matrix Information Theory for Self-Supervised LearningCode1
KOVIS: Keypoint-based Visual Servoing with Zero-Shot Sim-to-Real Transfer for Robotics ManipulationCode1
Mitigating Memorization of Noisy Labels via Regularization between RepresentationsCode1
Self-supervised Learning from a Multi-view PerspectiveCode1
AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech RepresentationCode1
Delving Deep into the Generalization of Vision Transformers under Distribution ShiftsCode1
Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised LearningCode1
Crowdsourced 3D Mapping: A Combined Multi-View Geometry and Self-Supervised Learning ApproachCode1
2nd Place Solution to Facebook AI Image Similarity Challenge Matching TrackCode1
K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and SyllablesCode1
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual RepresentationsCode1
Label-Efficient Learning in Agriculture: A Comprehensive ReviewCode1
A vector quantized masked autoencoder for speech emotion recognitionCode1
LAFS: Landmark-based Facial Self-supervised Learning for Face RecognitionCode1
LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic AgentsCode1
Language modeling via stochastic processesCode1
DeLoRes: Decorrelating Latent Spaces for Low-Resource Audio Representation LearningCode1
Wiener Graph Deconvolutional Network Improves Graph Self-Supervised LearningCode1
AVF-MAE++: Scaling Affective Video Facial Masked Autoencoders via Efficient Audio-Visual Self-Supervised LearningCode1
Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G NetworksCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image SegmentationCode1
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from ImageCode1
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent MaskingCode1
Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking ConsistencyCode1
D2C: Diffusion-Decoding Models for Few-Shot Conditional GenerationCode1
An Embarrassingly Simple Backdoor Attack on Self-supervised LearningCode1
D2C: Diffusion-Denoising Models for Few-shot Conditional GenerationCode1
DABS: A Domain-Agnostic Benchmark for Self-Supervised LearningCode1
DailyMAE: Towards Pretraining Masked Autoencoders in One DayCode1
DAS-N2N: Machine learning Distributed Acoustic Sensing (DAS) signal denoising without clean dataCode1
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and LanguageCode1
data2vec-aqc: Search for the right Teaching Assistant in the Teacher-Student training setupCode1
Backdoor Defense via Decoupling the Training ProcessCode1
Data Augmentation for Object Detection via Differentiable Neural RenderingCode1
Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow EstimationCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition ProblemsCode1
DATA: Domain-Aware and Task-Aware Self-supervised LearningCode1
Learning Continuous Representation of Audio for Arbitrary Scale Super ResolutionCode1
BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised LearningCode1
AnatoMask: Enhancing Medical Image Segmentation with Reconstruction-guided Self-maskingCode1
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the LeastCode1
BADGR: An Autonomous Self-Supervised Learning-Based Navigation SystemCode1
Data-Efficient Reinforcement Learning with Self-Predictive RepresentationsCode1
Bag of Instances Aggregation Boosts Self-supervised DistillationCode1
Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image AnalysisCode1
Self-Adaptive Training: Bridging Supervised and Self-Supervised LearningCode1
Machine Learning for the Digital Typhoon Dataset: Extensions to Multiple Basins and New Developments in Representations and TasksCode1
Animating Landscape: Self-Supervised Learning of Decoupled Motion and Appearance for Single-Image Video SynthesisCode1
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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