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 5576–5600 of 6661 papers

TitleStatusHype
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning—0
Contrastive Learning for Low Resource Machine Translation—0
Neighbour Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification—0
ICLEA: Interactive Contrastive Learning for Self-supervised Entity Alignment—0
Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text—0
Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense Inference—0
Enhancing the Nonlinear Mutual Dependencies in Transformers with Mutual Information—0
Detect Low-Resource Rumors in Microblog Posts via Adversarial Contrastive Learning—0
Control False Negative Instances In Contrastive Learning To ImproveLong-tailed Item Categorization—0
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search—0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Robust Pretrained Language Models—0
More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching—0
UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining—0
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings—0
Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings—0
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching—0
Improving Word Translation via Two-Stage Contrastive LearningCode1
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition—0
Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning—0
Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning—0
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue—0
KNN-BERT: Fine-Tuning Pre-Trained Models with KNN Classifier—0
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval—0
Prototypical Verbalizer for Prompt-based Few-shot Tuning—0
Contrastive Conditional Masked Language Model for Non-autoregressive Neural Machine 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