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 4201–4250 of 5044 papers

TitleStatusHype
Domain-aware Self-supervised Pre-training for Weakly-supervised Meme Analysis—0
QK Iteration: A Self-Supervised Representation Learning Algorithm for Image Similarity—0
Large-Scale Hyperspectral Image Clustering Using Contrastive LearningCode0
Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning—0
Robust and Accurate Object Detection via Self-Knowledge DistillationCode0
The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
Membership Inference Attacks Against Self-supervised Speech ModelsCode0
Characterizing the adversarial vulnerability of speech self-supervised learning—0
Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation—0
Hybrid BYOL-ViT: Efficient approach to deal with small datasets—0
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data AugmentationsCode0
Leveraging Time Irreversibility with Order-Contrastive Pre-training—0
Multi-Airport Delay Prediction with Transformers—0
On the Role of Corpus Ordering in Language Modeling—0
Self-Supervised Radio-Visual Representation Learning for 6G Sensing—0
Parameter-Efficient Domain Knowledge Integration from Multiple Sources for Biomedical Pre-trained Language Models—0
Distilling Word Meaning in Context from Pre-trained Language ModelsCode0
Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation—0
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood PredictionCode0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering—0
10 Security and Privacy Problems in Large Foundation Models—0
GenURL: A General Framework for Unsupervised Representation Learning—0
MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition—0
Directional Self-supervised Learning for Heavy Image Augmentations—0
Understanding the Role of Self-Supervised Learning in Out-of-Distribution Detection Task—0
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks—0
2nd Place Solution for SODA10M Challenge 2021 -- Continual Detection Track—0
Self-Supervised Visual Representation Learning Using Lightweight Architectures—0
Knowledge distillation from language model to acoustic model: a hierarchical multi-task learning approach—0
Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model—0
Zero-Shot Physics-Guided Deep Learning for Subject-Specific MRI Reconstruction—0
Constrained Mean Shift for Representation Learning—0
SSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric—0
Speech Representation Learning Through Self-supervised Pretraining And Multi-task Finetuning—0
DECAR: Deep Clustering for learning general-purpose Audio RepresentationsCode0
SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging—0
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes—0
Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks—0
Continual Learning on Noisy Data Streams via Self-Purified ReplayCode0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy LabelsCode0
The Impact of Spatiotemporal Augmentations on Self-Supervised Audiovisual Representation Learning—0
Multi-Modal Pre-Training for Automated Speech Recognition—0
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition—0
SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition—0
Towards Safer Transportation: a self-supervised learning approach for traffic video deraining—0
Neural Algorithmic Reasoners are Implicit Planners—0
Towards Demystifying Representation Learning with Non-contrastive Self-supervisionCode0
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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