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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1301–1310 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
DistilCSE: Effective Knowledge Distillation For Contrastive Sentence Embeddings	Dec 10, 2021	Contrastive LearningKnowledge Distillation	CodeCode Available	1	5
Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction	Sep 3, 2022	Contrastive LearningInductive Link Prediction	CodeCode Available	1	5
DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning	Apr 19, 2021	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning	Jul 7, 2023	Contrastive Learningreinforcement-learning	CodeCode Available	1	5
Contrastive Learning for Prompt-Based Few-Shot Language Learners	May 3, 2022	Contrastive LearningIn-Context Learning	CodeCode Available	1	5
Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data	Apr 19, 2022	Contrastive LearningDiversity	CodeCode Available	1	5
Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning	Jul 20, 2022	Action DetectionAction Recognition	CodeCode Available	1	5
An Efficient Self-Supervised Cross-View Training For Sentence Embedding	Nov 6, 2023	Contrastive LearningLanguage Modeling	CodeCode Available	1	5
Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation	Oct 12, 2021	Contrastive LearningSequential Recommendation	CodeCode Available	1	5
Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification	May 26, 2023	ClassificationContrastive Learning	CodeCode Available	1	5

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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