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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 6001–6025 of 6661 papers

TitleStatusHype
Contrastive Learning for Low Resource Machine Translation—0
Learning Universal Sentence Embeddings with Large-scale Parallel Translation Datasets—0
Textual Entailment with Dynamic Contrastive Learning for Zero-shot NER—0
Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense Inference—0
Prototypical Verbalizer for Prompt-based Few-shot Tuning—0
Debiased Contrastive Learning of Unsupervised Sentence Representations—0
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue—0
UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining—0
KNN-BERT: Fine-Tuning Pre-Trained Models with KNN Classifier—0
More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching—0
Contrastive Conditional Masked Language Model for Non-autoregressive Neural Machine Translation—0
UNICON: Unsupervised Intent Discovery via Semantic-level Contrastive Learning—0
Alleviating the Sparsity of Open Knowledge Graphs with Pretrained Contrastive Learning—0
ReadE: Learning Relation-Dependent Entity Representation for Knowledge Graph Completion—0
A Unified and Efficient Contrastive Learning Framework for Text Summarization—0
Enhancing the Nonlinear Mutual Dependencies in Transformers with Mutual Information—0
ConTFV: A Contrastive Learning Framework for Table-based Fact Verification—0
Improving Neural Topic Models by Contrastive Learning with BERT—0
Unsupervised Domain Adaptation with Contrastive Learning for Cross-domain Chinese NER—0
Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning—0
Repo4QA: Answering Complex Coding Questions via Dense Retrieval on GitHub Repositories—0
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching—0
ICLEA: Interactive Contrastive Learning for Self-supervised Entity Alignment—0
Utterance Rewriting with Contrastive Learning in Multi-turn Dialogue—0
Control False Negative Instances In Contrastive Learning To ImproveLong-tailed Item Categorization—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