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 4251–4300 of 5044 papers

TitleStatusHype
Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation—0
A Learnable Self-supervised Task for Unsupervised Domain Adaptation on Point Clouds—0
Towards Fine-grained Visual Representations by Combining Contrastive Learning with Image Reconstruction and Attention-weighted PoolingCode1
Stereo Matching by Self-supervision of Multiscopic Vision—0
SiT: Self-supervised vIsion TransformerCode1
CoCoNets: Continuous Contrastive 3D Scene RepresentationsCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
Multiple Object Tracking with Correlation Learning—0
Speech Denoising Without Clean Training Data: A Noise2Noise ApproachCode1
Self-supervised Learning of Depth Inference for Multi-view StereoCode1
Representative & Fair Synthetic Data—0
S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained RepresentationsCode1
Self-Supervised Learning for Gastritis Detection with Gastric X-ray Images—0
Self-Supervised Learning for Semi-Supervised Temporal Action ProposalCode1
Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs—0
Strumming to the Beat: Audio-Conditioned Contrastive Video Textures—0
Action Shuffle Alternating Learning for Unsupervised Action Segmentation—0
Efficient Personalized Speech Enhancement through Self-Supervised Learning—0
Personalized Speech Enhancement through Self-Supervised Data Augmentation and Purification—0
An Empirical Study of Training Self-Supervised Vision TransformersCode1
Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments—0
Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training—0
The Spatially-Correlative Loss for Various Image Translation TasksCode1
Keep Learning: Self-supervised Meta-learning for Learning from Inference—0
Self-supervised Motion Learning from Static Images—0
LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic AgentsCode1
Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoderCode1
Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain AdaptationCode1
On the Origin of Species of Self-Supervised Learning—0
Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition—0
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing DataCode1
Neural Transformation Learning for Deep Anomaly Detection Beyond ImagesCode1
Broaden Your Views for Self-Supervised Video LearningCode1
Source-Free Domain Adaptation for Semantic Segmentation—0
Benchmarking Representation Learning for Natural World Image CollectionsCode0
Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI dataCode0
Robust Audio-Visual Instance Discrimination—0
Category-Adaptive Domain Adaptation for Semantic Segmentation—0
Classification of Seeds using Domain Randomization on Self-Supervised Learning Frameworks—0
Towards High Fidelity Monocular Face Reconstruction with Rich Reflectance using Self-supervised Learning and Ray Tracing—0
Representation Learning by Ranking under multiple tasks—0
Friends and Foes in Learning from Noisy Labels—0
Self-supervised Graph Neural Networks without explicit negative samplingCode1
Quantum Self-Supervised LearningCode1
Contrastive Domain Adaptation—0
Unsupervised Document Embedding via Contrastive Augmentation—0
Self-Supervised Learning in Multi-Task Graphs through Iterative Consensus ShiftCode0
Residual Energy-Based Models for End-to-End Speech Recognition—0
Rethinking Self-Supervised Learning: Small is BeautifulCode1
Vectorization and Rasterization: Self-Supervised Learning for Sketch and HandwritingCode1
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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