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 1071–1080 of 6661 papers

Title	Date	Tasks	Status	Hype
Enhancing Text-based Knowledge Graph Completion with Zero-Shot Large Language Models: A Focus on Semantic Enhancement	Oct 12, 2023	Contrastive LearningData Augmentation	CodeCode Available	1
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization	Oct 30, 2020	Contrastive LearningGeneral Classification	CodeCode Available	1
Temporal Context Aggregation for Video Retrieval with Contrastive Learning	Aug 4, 2020	Contrastive LearningRepresentation Learning	CodeCode Available	1
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings	May 23, 2023	Community DetectionContrastive Learning	CodeCode Available	1
Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations	Apr 1, 2022	ClusteringContrastive Learning	CodeCode Available	1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning	Jun 9, 2022	Contrastive LearningGraph Representation Learning	CodeCode Available	1
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought	Sep 20, 2023	Contrastive LearningDenoising	CodeCode Available	1
Contrastive Domain Adaptation for Time-Series via Temporal Mixup	Dec 3, 2022	Contrastive LearningDomain Adaptation	CodeCode Available	1
Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph Completion	Jul 3, 2024	Contrastive LearningKnowledge Graph Completion	CodeCode Available	1
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding	Sep 22, 2022	Contrastive LearningVideo Grounding	CodeCode Available	1

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All datasets imagenet-1k 10,000 People - Human Pose Recognition Data CIFAR-10 STL-10

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet50	ImageNet Top-1 Accuracy	73.6	—	Unverified
2	ResNet50	ImageNet Top-1 Accuracy	73	—	Unverified
3	ResNet50	ImageNet Top-1 Accuracy	71.1	—	Unverified
4	ResNet50	ImageNet Top-1 Accuracy	69.3	—	Unverified
5	ResNet50 (v2)	ImageNet Top-1 Accuracy	67.6	—	Unverified
6	ResNet50 (v2)	ImageNet Top-1 Accuracy	63.8	—	Unverified
7	ResNet50	ImageNet Top-1 Accuracy	63.6	—	Unverified
8	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
9	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
10	ResNet50 (4×)	ImageNet Top-1 Accuracy	61.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1	0..5sec	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	84.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	85.55	—	Unverified