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 3901–3950 of 5044 papers

TitleStatusHype
Ensembles and Encoders for Task-Free Continual Learning—0
Encoding Event-Based Gesture Data With a Hybrid SNN Guided Variational Auto-encoder—0
Boundary-aware Pre-training for Video Scene Segmentation—0
A theoretically grounded characterization of feature representations—0
3D Pre-training improves GNNs for Molecular Property Prediction—0
Referring Self-supervised Learning on 3D Point Cloud—0
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching—0
One Objective for All Models --- Self-supervised Learning for Topic Models—0
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK—0
LEARNING PHONEME-LEVEL DISCRETE SPEECH REPRESENTATION WITH WORD-LEVEL SUPERVISION—0
AAVAE: Augmentation-Augmented Variational Autoencoders—0
Multi-Domain Self-Supervised Learning—0
Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning—0
Self-Supervision Enhanced Feature Selection with Correlated Gates—0
Self-Supervised Learning of Motion-Informed Latents—0
Noisy Adversarial Training—0
Learnability and Expressiveness in Self-Supervised Learning—0
PASS: Patch-Aware Self-Supervision for Vision Transformer—0
Environment Predictive Coding for Visual Navigation—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations—0
ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning—0
Learning Minimal Representations with Model Invariance—0
Self-GenomeNet: Self-supervised Learning with Reverse-Complement Context Prediction for Nucleotide-level Genomics Data—0
Understanding Self-supervised Learning via Information Bottleneck Principle—0
Data-Efficient Contrastive Learning by Differentiable Hard Sample and Hard Positive Pair Generation—0
Joint Self-Supervised Learning for Vision-based Reinforcement Learning—0
SimMER: Simple Maximization of Entropy and Rank for Self-supervised Representation Learning—0
Chaos is a Ladder: A New Understanding of Contrastive Learning—0
Vi-MIX FOR SELF-SUPERVISED VIDEO REPRESENTATION—0
Sphere2Vec: Self-Supervised Location Representation Learning on Spherical Surfaces—0
How Does SimSiam Avoid Collapse Without Negative Samples? Towards a Unified Understanding of Progress in SSL—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised image denoising—0
Measuring the Effectiveness of Self-Supervised Learning using Calibrated Learning Curves—0
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning—0
Rethinking Temperature in Graph Contrastive LearningCode0
Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo Dropout—0
Adaptive Multi-layer Contrastive Graph Neural Networks—0
Learning Background Invariance Improves Generalization and Robustness in Self-Supervised Learning on ImageNet and Beyond—0
How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?—0
The Tensor Brain: A Unified Theory of Perception, Memory and Semantic DecodingCode0
Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training FrameworkCode1
Multi-source Few-shot Domain Adaptation—0
How much human-like visual experience do current self-supervised learning algorithms need in order to achieve human-level object recognition?Code0
Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks—0
Self-supervised Learning for Semi-supervised Temporal Language Grounding—0
Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite RulesCode0
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised LearningCode1
DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder—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