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 4851–4875 of 6661 papers

TitleStatusHype
False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation—0
FAMSeC: A Few-shot-sample-based General AI-generated Image Detection Method—0
FARM: Frequency-Aware Model for Cross-Domain Live-Streaming Recommendation—0
FARM: Functional Group-Aware Representations for Small Molecules—0
FastCAD: Real-Time CAD Retrieval and Alignment from Scans and Videos—0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
Fast Training of Contrastive Learning with Intermediate Contrastive Loss—0
FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems—0
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification—0
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look—0
Feature-Aware Noise Contrastive Learning for Unsupervised Red Panda Re-Identification—0
Feature Distillation With Guided Adversarial Contrastive Learning—0
Feature Extraction Framework based on Contrastive Learning with Adaptive Positive and Negative Samples—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
Features-over-the-Air: Contrastive Learning Enabled Cooperative Edge Inference—0
FecTek: Enhancing Term Weight in Lexicon-Based Retrieval with Feature Context and Term-level Knowledge—0
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation—0
Self-supervised On-device Federated Learning from Unlabeled Streams—0
FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
FedEPA: Enhancing Personalization and Modality Alignment in Multimodal Federated Learning—0
Federated Contrastive Learning for Decentralized Unlabeled Medical Images—0
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning—0
Federated Contrastive Learning for Personalized Semantic Communication—0
Federated Contrastive Learning for Privacy-Preserving Unpaired Image-to-Image Translation—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