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 3351–3400 of 5044 papers

TitleStatusHype
Self-Supervised Learning for Stereo Matching with Self-Improving Ability—0
Self-Supervised Learning for Text Recognition: A Critical Survey—0
Self-supervised Learning for Unintentional Action Prediction—0
Self-Supervised Learning for User Localization—0
Self-Supervised Learning for Visual Summary Identification in Scientific Publications—0
Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition: A Systematic Study—0
Self-supervised Learning from 100 Million Medical Images—0
Self-Supervised Learning from Semantically Imprecise Data—0
Self-Supervised Learning from Unlabeled Fundus Photographs Improves Segmentation of the Retina—0
Self-supervised Learning: Generative or Contrastive—0
Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancers—0
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences—0
Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification—0
Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy—0
Self-supervised Learning of 3D Object Understanding by Data Association and Landmark Estimation for Image Sequence—0
Self-supervised Learning of 3D Objects from Natural Images—0
Self-Supervised Learning of a Biologically-Inspired Visual Texture Model—0
Self-Supervised Learning of Action Affordances as Interaction Modes—0
Self-Supervised Learning of Appliance Usage—0
Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading—0
Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking—0
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment—0
Self-supervised learning of class embeddings from video—0
Self-Supervised Learning of Compressed Video Representations—0
Self-Supervised Learning of Depth and Camera Motion from 360° Videos—0
Self-Supervised Learning of Depth and Ego-motion with Differentiable Bundle Adjustment—0
Self-Supervised Learning of Depth and Ego-Motion from Video by Alternative Training and Geometric Constraints from 3D to 2D—0
Self-Supervised Learning of Deviation in Latent Representation for Co-speech Gesture Video Generation—0
Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series—0
Self-supervised Learning of Distance Functions for Goal-Conditioned Reinforcement Learning—0
Self-Supervised Learning of Domain Invariant Features for Depth Estimation—0
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks—0
Self-supervised Learning of Event-guided Video Frame Interpolation for Rolling Shutter Frames—0
Self-Supervised Learning of Gait-Based Biomarkers—0
Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection—0
Self-Supervised Learning of Graph Neural Networks: A Unified Review—0
Self-Supervised Learning of Grasping Arbitrary Objects On-the-Move—0
Self-supervised Learning of Implicit Shape Representation with Dense Correspondence for Deformable Objects—0
Self-supervised learning of inverse problem solvers in medical imaging—0
Self-Supervised Learning of Iterative Solvers for Constrained Optimization—0
Self-Supervised Learning of Linear Precoders under Non-Linear PA Distortion for Energy-Efficient Massive MIMO Systems—0
Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals—0
Self-Supervised Learning of Motion-Informed Latents—0
Self-Supervised Learning of Multi-Object Keypoints for Robotic Manipulation—0
Self-supervised learning of multi-omics embeddings in the low-label, high-data regime—0
Learning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization—0
Self-supervised Learning of Neural Implicit Feature Fields for Camera Pose Refinement—0
Self-Supervised Learning of Non-Rigid Residual Flow and Ego-Motion—0
Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos—0
Self-Supervised Learning of Object Motion Through Adversarial Video Prediction—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