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 4876–4900 of 6661 papers

TitleStatusHype
Federated Contrastive Learning for Volumetric Medical Image Segmentation—0
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading—0
Federated Contrastive Learning of Graph-Level Representations—0
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching—0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
Federated Generalized Category Discovery—0
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning—0
Federated Hyperdimensional Computing—0
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach—0
Federated Prototype Graph Learning—0
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning—0
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis—0
FedIFL: A federated cross-domain diagnostic framework for motor-driven systems with inconsistent fault modes—0
FedRGL: Robust Federated Graph Learning for Label Noise—0
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning—0
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation—0
FedSiam-DA: Dual-aggregated Federated Learning via Siamese Network under Non-IID Data—0
FedSSC: Shared Supervised-Contrastive Federated Learning—0
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis—0
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services—0
Fermi-Bose Machine achieves both generalization and adversarial robustness—0
Few-Example Clustering via Contrastive Learning—0
Few-Shot Classification with Contrastive Learning—0
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images—0
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning—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