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 4401–4450 of 5044 papers

TitleStatusHype
Scaling and Benchmarking Self-Supervised Visual Representation LearningCode0
On the Stepwise Nature of Self-Supervised LearningCode0
On the Role of Discrete Tokenization in Visual Representation LearningCode0
On the Out-of-Distribution Generalization of Self-Supervised LearningCode0
On the Importance of Embedding Norms in Self-Supervised LearningCode0
On the Generalization and Causal Explanation in Self-Supervised LearningCode0
On the Generalizability of Foundation Models for Crop Type MappingCode0
On the Difficulty of Defending Self-Supervised Learning against Model ExtractionCode0
Online Unsupervised Learning of Visual Representations and CategoriesCode0
Online Semi-Supervised Learning in Contextual Bandits with Episodic RewardCode0
FedRSU: Federated Learning for Scene Flow Estimation on Roadside UnitsCode0
Clustering-Based Representation Learning through Output Translation and Its Application to Remote--Sensing ImagesCode0
Object-Oriented Dynamics Learning through Multi-Level AbstractionCode0
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data HeterogeneityCode0
Object discovery and representation networksCode0
Unifying Synergies between Self-supervised Learning and Dynamic ComputationCode0
OAMixer: Object-aware Mixing Layer for Vision TransformersCode0
Novel Class Discovery: an Introduction and Key ConceptsCode0
SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene UnderstandingCode0
Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature SpaceCode0
Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical LearningCode0
Feature-Suppressed Contrast for Self-Supervised Food Pre-trainingCode0
SCORE: Self-supervised Correspondence Fine-tuning for Improved Content RepresentationsCode0
NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy LabelsCode0
Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton SequenceCode0
Scribble-supervised Cell Segmentation Using Multiscale Contrastive RegularizationCode0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
Noisier2Inverse: Self-Supervised Learning for Image Reconstruction with Correlated NoiseCode0
Noise-Robust Keyword Spotting through Self-supervised PretrainingCode0
SDLFormer: A Sparse and Dense Locality-enhanced Transformer for Accelerated MR Image ReconstructionCode0
Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite ImageryCode0
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood PredictionCode0
Neural Koopman prior for data assimilationCode0
Collaborative Unsupervised Visual Representation Learning from Decentralized DataCode0
Neural Identification for ControlCode0
Benchmarking Representation Learning for Natural World Image CollectionsCode0
An Empirical Study Of Self-supervised Learning Approaches For Object Detection With TransformersCode0
Neural Descriptors: Self-Supervised Learning of Robust Local Surface Descriptors Using Polynomial PatchesCode0
A Conceptual Bio-Inspired Framework for the Evolution of Artificial General IntelligenceCode0
Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal imagesCode0
Extracting speaker and emotion information from self-supervised speech models via channel-wise correlationsCode0
Exploring the Effect of Primitives for Compositional Generalization in Vision-and-LanguageCode0
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNsCode0
Neural Blind Deconvolution Using Deep PriorsCode0
Negative-Free Self-Supervised Gaussian Embedding of GraphsCode0
NearbyPatchCL: Leveraging Nearby Patches for Self-Supervised Patch-Level Multi-Class Classification in Whole-Slide ImagesCode0
NarrowBERT: Accelerating Masked Language Model Pretraining and InferenceCode0
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual RepresentationsCode0
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillationCode0
MVEB: Self-Supervised Learning with Multi-View Entropy BottleneckCode0
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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