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 2801–2850 of 5044 papers

TitleStatusHype
Homomorphic Self-Supervised Learning—0
Hopfield model with planted patterns: a teacher-student self-supervised learning model—0
HoughCL: Finding Better Positive Pairs in Dense Self-supervised Learning—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
How Does SimSiam Avoid Collapse Without Negative Samples? Towards a Unified Understanding of Progress in SSL—0
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning—0
How Effective are Self-Supervised Models for Contact Identification in Videos—0
How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks—0
How Robust is Unsupervised Representation Learning to Distribution Shift?—0
How Self-Supervised Learning Can be Used for Fine-Grained Head Pose Estimation?—0
How Should We Extract Discrete Audio Tokens from Self-Supervised Models?—0
How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario—0
How to learn from unlabeled volume data: Self-Supervised 3D Context Feature Learning—0
How to Scale Your EMA—0
How to Train Your CheXDragon: Training Chest X-Ray Models for Transfer to Novel Tasks and Healthcare Systems—0
How to Understand Masked Autoencoders—0
How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?—0
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot Learning?—0
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?—0
How You Move Your Head Tells What You Do: Self-supervised Video Representation Learning with Egocentric Cameras and IMU Sensors—0
Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks—0
Human Gaze Boosts Object-Centered Representation Learning—0
Human-Timescale Adaptation in an Open-Ended Task Space—0
Hybrid BYOL-ViT: Efficient approach to deal with small datasets—0
Hybrid Deep Learning and Signal Processing for Arabic Dialect Recognition in Low-Resource Settings—0
Hybrid Interest Modeling for Long-tailed Users—0
Hybrid Learning: A Novel Combination of Self-Supervised and Supervised Learning for MRI Reconstruction without High-Quality Training Reference—0
Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction—0
Hyperbolic Contrastive Learning—0
HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection—0
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior—0
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques—0
Hyperspherically Regularized Networks for Self-Supervision—0
iBoot: Image-bootstrapped Self-Supervised Video Representation Learning—0
Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models—0
Identifying Terrain Physical Parameters from Vision -- Towards Physical-Parameter-Aware Locomotion and Navigation—0
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning—0
Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning—0
Image-Based Vehicle Classification by Synergizing Features from Supervised and Self-Supervised Learning Paradigms—0
Image Classification Using a Diffusion Model as a Pre-Training Model—0
Image Coding for Machines with Omnipotent Feature Learning—0
Image Compression with Product Quantized Masked Image Modeling—0
Image Generation and Learning Strategy for Deep Document Forgery Detection—0
Image as First-Order Norm+Linear Autoregression: Unveiling Mathematical Invariance—0
Imaging with Equivariant Deep Learning—0
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations—0
IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications—0
Impact Analysis of the Use of Speech and Language Models Pretrained by Self-Supersivion for Spoken Language Understanding—0
Impact of Language Guidance: A Reproducibility Study—0
IMPA-HGAE:Intra-Meta-Path Augmented Heterogeneous Graph Autoencoder—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