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 2151–2200 of 5044 papers

TitleStatusHype
Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders—0
A Contrastive Learning Foundation Model Based on Perfectly Aligned Sample Pairs for Remote Sensing Images—0
Exploring the Potential of SSL Models for Sound Event Detection—0
COIN: Co-Cluster Infomax for Bipartite Graphs—0
Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation—0
Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages—0
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review—0
Low-Resource Self-Supervised Learning with SSL-Enhanced TTS—0
Coherent, super resolved radar beamforming using self-supervised learning—0
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task—0
Generalized Supervised Contrastive Learning—0
Exploring the Common Principal Subspace of Deep Features in Neural Networks—0
Exploring Temporal Granularity in Self-Supervised Video Representation Learning—0
Exploring StyleGAN Latent Space for Face Alignment with Limited Training Data—0
Assessing the State of Self-Supervised Human Activity Recognition using Wearables—0
Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising—0
Exploring SSL Discrete Tokens for Multilingual ASR—0
Cocktail HuBERT: Generalized Self-Supervised Pre-training for Mixture and Single-Source Speech—0
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study—0
Assessing the Performance of the DINOv2 Self-supervised Learning Vision Transformer Model for the Segmentation of the Left Atrium from MRI Images—0
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction—0
CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning—0
3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization—0
Exploring Self-Supervised Representation Ensembles for COVID-19 Cough Classification—0
ASR Bundestag: A Large-Scale political debate dataset in German—0
Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach—0
Low-rank Optimal Transport: Approximation, Statistics and Debiasing—0
Exploring Self-Supervised Multi-view Contrastive Learning for Speech Emotion Recognition with Limited Annotations—0
Coarse Is Better? A New Pipeline Towards Self-Supervised Learning with Uncurated Images—0
Exploring Relations in Untrimmed Videos for Self-Supervised Learning—0
Exploring Pre-trained General-purpose Audio Representations for Heart Murmur Detection—0
Aspect-Based Sentiment Analysis using Local Context Focus Mechanism with DeBERTa—0
Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling—0
Exploring learning environments for label\-efficient cancer diagnosis—0
Exploring Intrinsic Properties of Medical Images for Self-Supervised Binary Semantic Segmentation—0
20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction—0
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning—0
LPaintB: Learning to Paint from Self-Supervision—0
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning—0
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects—0
Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection—0
A Simplified Framework for Contrastive Learning for Node Representations—0
Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding—0
Exploring Efficient-tuning Methods in Self-supervised Speech Models—0
CNC-Net: Self-Supervised Learning for CNC Machining Operations—0
A Simple HMM with Self-Supervised Representations for Phone Segmentation—0
Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation—0
Clustering Properties of Self-Supervised Learning—0
Exploring Effective Mask Sampling Modeling for Neural Image Compression—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