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 2601–2650 of 6661 papers

TitleStatusHype
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis—0
Hypergraph Diffusion for High-Order Recommender Systems—0
I^2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation—0
Contrastive View Design Strategies to Enhance Robustness to Domain Shifts in Downstream Object Detection—0
Contrastive Video Textures—0
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection—0
Contrastive Video-Language Segmentation—0
Can Semantic Labels Assist Self-Supervised Visual Representation Learning?—0
Contrastive Unsupervised Learning for Audio Fingerprinting—0
A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives—0
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System—0
Can representation learning for multimodal image registration be improved by supervision of intermediate layers?—0
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training—0
Hyperbolic Face Anti-Spoofing—0
Hyperbolic Hierarchical Contrastive Hashing—0
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?—0
Can Contrastive Learning Refine Embeddings—0
Contrastive String Representation Learning using Synthetic Data—0
A Multi-Source Heterogeneous Knowledge Injected Prompt Learning Method for Legal Charge Prediction—0
Hyperbolic Image-and-Pointcloud Contrastive Learning for 3D Classification—0
Contrastive Speaker Embedding With Sequential Disentanglement—0
Contrastive Speaker-Aware Learning for Multi-party Dialogue Generation with LLMs—0
Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework—0
Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces—0
Camera-aware Style Separation and Contrastive Learning for Unsupervised Person Re-identification—0
Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation—0
Approximate Bayesian Computation via Classification—0
Contrastive Self-Supervised Learning for Spatio-Temporal Analysis of Lung Ultrasound Videos—0
Apple of Sodom: Hidden Backdoors in Superior Sentence Embeddings via Contrastive Learning—0
A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversion—0
Hyperbolic Contrastive Learning—0
Contrastive Self-Supervised Learning As Neural Manifold Packing—0
Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections—0
CAMBranch: Contrastive Learning with Augmented MILPs for Branching—0
Hyperbolic Contrastive Learning for Hierarchical 3D Point Cloud Embedding—0
HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views—0
IB-DRR: Incremental Learning with Information-Back Discrete Representation Replay—0
Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence—0
Contrastive Representation Learning for Hand Shape Estimation—0
CalibrationPhys: Self-supervised Video-based Heart and Respiratory Rate Measurements by Calibrating Between Multiple Cameras—0
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities—0
Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation—0
Contrastive Representation Learning for 3D Protein Structures—0
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning—0
Contrastive Representation Learning for Acoustic Parameter Estimation—0
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation—0
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs—0
Contrastive Representation Learning: A Framework and Review—0
CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting—0
A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction—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