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 1651–1700 of 5044 papers

TitleStatusHype
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series—0
AACP: Aesthetics assessment of children's paintings based on self-supervised learning—0
Contrastive Learning from Demonstrations—0
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency—0
Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables—0
Improving Streaming Transformer Based ASR Under a Framework of Self-supervised Learning—0
Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning—0
Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control—0
Contrastive General Graph Matching with Adaptive Augmentation Sampling—0
Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Contrastive Domain Adaptation—0
Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices—0
Contrastive Continuity on Augmentation Stability Rehearsal for Continual Self-Supervised Learning—0
A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning—0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Improving Transformer-based Sequential Recommenders through Preference Editing—0
Category-Adaptive Domain Adaptation for Semantic Segmentation—0
Learning Video Representations using Contrastive Bidirectional Transformer—0
Improving self-supervised representation learning via sequential adversarial masking—0
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D—0
Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods—0
Contrastive Abstraction for Reinforcement Learning—0
Gen-SIS: Generative Self-augmentation Improves Self-supervised Learning—0
Contrast and Order Representations for Video Self-Supervised Learning—0
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning—0
GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing—0
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
Generative Deduplication For Socia Media Data Selection—0
Active Gaze Behavior Boosts Self-Supervised Object Learning—0
HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport—0
GenURL: A General Framework for Unsupervised Representation Learning—0
Embodiment: Self-Supervised Depth Estimation Based on Camera Models—0
GEO-BLEU: Similarity Measure for Geospatial Sequences—0
Improving Sentence Representations with Consensus Maximisation—0
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning—0
Continual Vision-Language Representation Learning with Off-Diagonal Information—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass—0
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning—0
A-JEPA: Joint-Embedding Predictive Architecture Can Listen—0
Generating Music Medleys via Playing Music Puzzle Games—0
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework—0
GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning—0
Continual Self-Supervised Learning with Masked Autoencoders in Remote Sensing—0
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Continual Self-supervised Learning Considering Medical Domain Knowledge in Chest CT Images—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