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 5526–5550 of 6661 papers

TitleStatusHype
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE—0
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations—0
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers—0
Leveraging Contrastive Learning for Few-shot Geolocation of Social Posts—0
Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances—0
Leveraging Diverse Modeling Contexts with Collaborating Learning for Neural Machine Translation—0
Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation—0
Leveraging Intra-modal and Inter-modal Interaction for Multi-Modal Entity Alignment—0
Leveraging large language models for efficient representation learning for entity resolution—0
Leveraging LLMs for Multimodal Retrieval-Augmented Radiology Report Generation via Key Phrase Extraction—0
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding—0
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach—0
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation—0
Leveraging Retrieval-Augmented Tags for Large Vision-Language Understanding in Complex Scenes—0
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection—0
Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT—0
Leveraging Superfluous Information in Contrastive Representation Learning—0
Leveraging Task Dependency and Contrastive Learning for Case Outcome Classification on European Court of Human Rights Cases—0
Leveraging the Third Dimension in Contrastive Learning—0
LIDIA: Precise Liver Tumor Diagnosis on Multi-Phase Contrast-Enhanced CT via Iterative Fusion and Asymmetric Contrastive Learning—0
Lifelong Person Re-Identification with Backward-Compatibility—0
Link-based Contrastive Learning for One-Shot Unsupervised Domain Adaptation—0
Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning—0
Linking Representations with Multimodal Contrastive Learning—0
LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection—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