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 611–620 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
AD-CLIP: Adapting Domains in Prompt Space Using CLIP	Aug 10, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	1	5
CLCC: Contrastive Learning for Color Constancy	Jun 9, 2021	Color ConstancyContrastive Learning	CodeCode Available	1	5
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval	Apr 21, 2024	Contrastive LearningConversational Search	CodeCode Available	1	5
Big Self-Supervised Models Advance Medical Image Classification	Jan 13, 2021	ClassificationContrastive Learning	CodeCode Available	1	5
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation	May 23, 2023	Contrastive LearningMachine Translation	CodeCode Available	1	5
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries	Nov 17, 2020	Contrastive LearningLinear evaluation	CodeCode Available	1	5
CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations	May 1, 2023	Contrastive Learningimage-classification	CodeCode Available	1	5
Towards Cross-Table Masked Pretraining for Web Data Mining	Jul 10, 2023	Contrastive Learning	CodeCode Available	1	5
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment	Dec 2, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
Cross-Domain Sentiment Classification with In-Domain Contrastive Learning	Dec 5, 2020	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