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 2701–2750 of 5044 papers

TitleStatusHype
Fusion of stereo and still monocular depth estimates in a self-supervised learning context—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—0
FUSSL: Fuzzy Uncertain Self Supervised Learning—0
Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report—0
GAIA: A Foundation Model for Operational Atmospheric Dynamics—0
GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes—0
Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models—0
Game State Learning via Game Scene Augmentation—0
GANORCON: Are Generative Models Useful for Few-shot Segmentation?—0
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition—0
Gated Self-supervised Learning For Improving Supervised Learning—0
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting—0
Gaussian Masked Autoencoders—0
Self-supervised learning of hologram reconstruction using physics consistency—0
GEDI: GEnerative and DIscriminative Training for Self-Supervised Learning—0
GelFlow: Self-supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
GenDistiller: Distilling Pre-trained Language Models based on an Autoregressive Generative Model—0
Generalised Co-Salient Object Detection—0
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning—0
Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Framework—0
Generalizable Re-Identification from Videos with Cycle Association—0
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification—0
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework—0
Generating Music Medleys via Playing Music Puzzle Games—0
Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
Generative Deduplication For Socia Media Data Selection—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning—0
GenURL: A General Framework for Unsupervised Representation Learning—0
GEO-BLEU: Similarity Measure for Geospatial Sequences—0
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D—0
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning—0
Category-Adaptive Domain Adaptation for Semantic Segmentation—0
Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices—0
GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning—0
Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language Models—0
Adapting Amidst Degradation: Cross Domain Li-ion Battery Health Estimation via Physics-Guided Test-Time Training—0
Grafit: Learning fine-grained image representations with coarse labels—0
Granularity-aware Adaptation for Image Retrieval over Multiple Tasks—0
Graph Adversarial Self-Supervised Learning—0
Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings—0
Graph Anomaly Detection via Adaptive Test-time Representation Learning across Out-of-Distribution Domains—0
Graph-Based Bidirectional Transformer Decision Threshold Adjustment Algorithm for Class-Imbalanced Molecular Data—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
GraphCL: Contrastive Self-Supervised Learning of Graph Representations—0
Graph Contrastive Learning with Generative Adversarial Network—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