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

Showing 351400 of 5044 papers

TitleStatusHype
A Self-supervised Method for Entity AlignmentCode1
A Fast Knowledge Distillation Framework for Visual RecognitionCode1
3D Self-Supervised Methods for Medical ImagingCode1
Contrastive Self-supervised Sequential Recommendation with Robust AugmentationCode1
A comprehensive survey on deep active learning in medical image analysisCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation LearningCode1
A Simple and Efficient Baseline for Data Attribution on ImagesCode1
Contextually Affinitive Neighborhood Refinery for Deep ClusteringCode1
A Simple Baseline for Low-Budget Active LearningCode1
Context Matters: Graph-based Self-supervised Representation Learning for Medical ImagesCode1
A simple, efficient and scalable contrastive masked autoencoder for learning visual representationsCode1
Continual Learning, Fast and SlowCode1
Container: Context Aggregation NetworksCode1
Physics-informed Temporal Alignment for Auto-regressive PDE Foundation ModelsCode1
CounTR: Transformer-based Generalised Visual CountingCode1
A Self-Correcting Sequential RecommenderCode1
EXAONEPath 1.0 Patch-level Foundation Model for PathologyCode1
Consistent Explanations by Contrastive LearningCode1
Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous DrivingCode1
Consistency-based Self-supervised Learning for Temporal Anomaly LocalizationCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
Container: Context Aggregation NetworkCode1
Context-Aware Sequence Alignment using 4D Skeletal AugmentationCode1
CrossTransformers: spatially-aware few-shot transferCode1
Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised LearningCode1
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual RepresentationsCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
A Study on Incorporating Whisper for Robust Speech AssessmentCode1
D2C: Diffusion-Decoding Models for Few-Shot Conditional GenerationCode1
DABS: A Domain-Agnostic Benchmark for Self-Supervised LearningCode1
A Surface Geometry Model for LiDAR Depth CompletionCode1
Contrastive Graph Learning for Population-based fMRI ClassificationCode1
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the LeastCode1
Contrastive prediction strategies for unsupervised segmentation and categorization of phonemes and wordsCode1
Deciphering and integrating invariants for neural operator learning with various physical mechanismsCode1
Concept Generalization in Visual Representation LearningCode1
Decoupled Contrastive LearningCode1
A Survey on Self-supervised Learning: Algorithms, Applications, and Future TrendsCode1
Adversarial Self-Supervised Contrastive LearningCode1
3rd Place: A Global and Local Dual Retrieval Solution to Facebook AI Image Similarity ChallengeCode1
Deep Unfolded Tensor Robust PCA with Self-supervised LearningCode1
A Survey of World Models for Autonomous DrivingCode1
Defending Against Patch-based Backdoor Attacks on Self-Supervised LearningCode1
Benchmarking Pathology Feature Extractors for Whole Slide Image ClassificationCode1
Delving Deep into the Generalization of Vision Transformers under Distribution ShiftsCode1
A Survey on Deep Multi-modal Learning for Body Language Recognition and GenerationCode1
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology ImagesCode1
AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model AlignmentCode1
Comparing Self-Supervised Learning Techniques for Wearable Human Activity RecognitionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pretraining: NoneImages & Text57.5Unverified
2Pretraining: ShEDImages & Text54.3Unverified
3Pretraining: e-MixImages & Text48.9Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy91.7Unverified
2ResNet18Accuracy91.02Unverified
3MV-MRAccuracy89.67Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy93.89Unverified
2ResNet18average top-1 classification accuracy92.58Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy72.51Unverified
2ResNet18average top-1 classification accuracy69.31Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy82.64Unverified
2CorInfomax (ResNet18)Top-1 Accuracy80.48Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy51.84Unverified
2ResNet18average top-1 classification accuracy51.67Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy93.18Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy71.61Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid BYOL-S/CvTAccuracy67.2Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy54.86Unverified