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 3001–3050 of 5044 papers

TitleStatusHype
Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning—0
Astromer 2—0
A Study of Forward-Forward Algorithm for Self-Supervised Learning—0
A Study of the Generalizability of Self-Supervised Representations—0
A study on the distribution of social biases in self-supervised learning visual models—0
A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment—0
A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation—0
A Survey of Deep Learning: From Activations to Transformers—0
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4—0
A Survey of Knowledge Enhanced Pre-trained Models—0
A Survey of Knowledge Enhanced Pre-trained Language Models—0
A Survey of Multilingual Models for Automatic Speech Recognition—0
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images—0
A Survey of the Self Supervised Learning Mechanisms for Vision Transformers—0
A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks—0
A Survey on Contrastive Self-supervised Learning—0
A Survey on Deep Hashing Methods—0
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond—0
A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis—0
A survey on Self Supervised learning approaches for improving Multimodal representation learning—0
A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks—0
Asymmetric Clean Segments-Guided Self-Supervised Learning for Robust Speaker Verification—0
A Tale of Color Variants: Representation and Self-Supervised Learning in Fashion E-Commerce—0
A theoretical framework for self-supervised contrastive learning for continuous dependent data—0
A theoretically grounded characterization of feature representations—0
A Theoretical Study of Inductive Biases in Contrastive Learning—0
A Theory of Self-Supervised Framework for Few-Shot Learning—0
AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning—0
Atmospheric Turbulence Removal with Video Sequence Deep Visual Priors—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Audio-Guided Fusion Techniques for Multimodal Emotion Analysis—0
Audio Self-supervised Learning: A Survey—0
Audio-Visual Contrastive Learning with Temporal Self-Supervision—0
Audio-visual fine-tuning of audio-only ASR models—0
Audio-Visual Self-Supervised Terrain Type Discovery for Mobile Platforms—0
Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings—0
Audio-Visual Speech Enhancement Using Self-supervised Learning to Improve Speech Intelligibility in Cochlear Implant Simulations—0
AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image—0
Augmentation-Free Graph Contrastive Learning with Performance Guarantee—0
Augmentations vs Algorithms: What Works in Self-Supervised Learning—0
Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations—0
Unified Framework for Feature Extraction based on Contrastive Learning—0
A Unified Model For Voice and Accent Conversion In Speech and Singing using Self-Supervised Learning and Feature Extraction—0
Automated data curation for self-supervised learning in underwater acoustic analysis—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
Automated Measurement of Eczema Severity with Self-Supervised Learning—0
Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning—0
Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations—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