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 5401–5425 of 6661 papers

TitleStatusHype
Self-Supervised Beat Tracking in Musical Signals with Polyphonic Contrastive Learning—0
Advancing 3D Medical Image Analysis with Variable Dimension Transform based Supervised 3D Pre-trainingCode1
C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender SystemCode1
Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization—0
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data AugmentationsCode1
Self-supervised Learning from 100 Million Medical Images—0
Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings—0
Effective Conditioned and Composed Image Retrieval Combining CLIP-Based FeaturesCode1
Cross-Patch Dense Contrastive Learning for Semi-Supervised Segmentation of Cellular Nuclei in Histopathologic ImagesCode1
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic SegmentationCode2
Harmony: A Generic Unsupervised Approach for Disentangling Semantic Content From Parameterized Transformations—0
Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation—0
EI-CLIP: Entity-Aware Interventional Contrastive Learning for E-Commerce Cross-Modal Retrieval—0
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency—0
Unleashing Potential of Unsupervised Pre-Training With Intra-Identity Regularization for Person Re-Identification—0
Multi-Marginal Contrastive Learning for Multi-Label Subcellular Protein LocalizationCode0
Contextual Outpainting With Object-Level Contrastive Learning—0
Id-Free Person Similarity Learning—0
UBoCo: Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection—0
PCL: Proxy-Based Contrastive Learning for Domain GeneralizationCode1
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization—0
Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning—0
Learning Video Representations of Human Motion From Synthetic Data—0
Knowledge-Driven Self-Supervised Representation Learning for Facial Action Unit Recognition—0
Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer—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