SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 5201–5225 of 6661 papers

TitleStatusHype
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation—0
Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data—0
Line Graph Contrastive Learning for Link PredictionCode0
Event-Centric Question Answering via Contrastive Learning and Invertible Event TransformationCode0
Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices—0
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems—0
Adversarial Pretraining of Self-Supervised Deep Networks: Past, Present and Future—0
Global Contrastive Batch Sampling via Optimization on Sample PermutationsCode0
A Benchmark Study of Contrastive Learning for Arabic Social MeaningCode0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)—0
GLCC: A General Framework for Graph-Level Clustering—0
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing—0
Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem—0
Apple of Sodom: Hidden Backdoors in Superior Sentence Embeddings via Contrastive Learning—0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification—0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
Controller-Guided Partial Label Consistency Regularization with Unlabeled Data—0
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP ModelsCode0
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive AdaptationCode0
UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood LearningCode0
HAVANA: Hard negAtiVe sAmples aware self-supervised coNtrastive leArning for Airborne laser scanning point clouds semantic segmentation—0
Supervised Contrastive Learning with Tree-Structured Parzen Estimator Bayesian Optimization for Imbalanced Tabular Data—0
Universal hidden monotonic trend estimation with contrastive learning—0
Rethinking Prototypical Contrastive Learning through Alignment, Uniformity and Correlation—0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified