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 3326–3350 of 6661 papers

TitleStatusHype
Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement—0
Confidence-aware Contrastive Learning for Selective ClassificationCode0
QAGCF: Graph Collaborative Filtering for Q&A Recommendation—0
Road Network Representation Learning with the Third Law of Geography—0
JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model EditsCode0
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential RecommendationCode0
ConPCO: Preserving Phoneme Characteristics for Automatic Pronunciation Assessment Leveraging Contrastive Ordinal Regularization—0
AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection—0
Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and BeyondCode0
RevRIR: Joint Reverberant Speech and Room Impulse Response Embedding using Contrastive Learning with Application to Room Shape Classification—0
Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing—0
MMCL: Boosting Deformable DETR-Based Detectors with Multi-Class Min-Margin Contrastive Learning for Superior Prohibited Item DetectionCode0
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning—0
Negative Prototypes Guided Contrastive Learning for WSOD—0
Personalized Topic Selection Model for Topic-Grounded Dialogue—0
RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts—0
SMCL: Saliency Masked Contrastive Learning for Long-tailed Recognition—0
Contrastive Language Video Time Pre-training—0
Towards Out-of-Distribution Detection in Vocoder Recognition via Latent Feature Reconstruction—0
DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic SurgeryCode0
Multi-Agent Transfer Learning via Temporal Contrastive Learning—0
Enhancing Inertial Hand based HAR through Joint Representation of Language, Pose and Synthetic IMUs—0
Unsupervised Contrastive Analysis for Salient Pattern Detection using Conditional Diffusion ModelsCode0
MGI: Multimodal Contrastive pre-training of Genomic and Medical Imaging—0
Effectiveness of Vision Language Models for Open-world Single Image Test Time Adaptation—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