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 3561–3570 of 6661 papers

Title	Date	Tasks	Status	Hype
Sample-Specific Debiasing for Better Image-Text Models	Apr 25, 2023	Contrastive LearningCross-Modal Retrieval	—Unverified	0
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning	Apr 25, 2023	Contrastive LearningFederated Learning	CodeCode Available	1
ContrastMotion: Self-supervised Scene Motion Learning for Large-Scale LiDAR Point Clouds	Apr 25, 2023	Autonomous DrivingContrastive Learning	—Unverified	0
Unsupervised Synthetic Image Refinement via Contrastive Learning and Consistent Semantic-Structural Constraints	Apr 25, 2023	BenchmarkingContrastive Learning	—Unverified	0
OFAR: A Multimodal Evidence Retrieval Framework for Illegal Live-streaming Identification	Apr 25, 2023	Contrastive LearningRetrieval	—Unverified	0
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning	Apr 25, 2023	Contrastive LearningTransfer Learning	—Unverified	0
CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis	Apr 25, 2023	Contrastive LearningVocal Bursts Type Prediction	CodeCode Available	1
Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation	Apr 24, 2023	Contrastive LearningMultimodal Recommendation	—Unverified	0
Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network	Apr 24, 2023	Contrastive LearningGraph Neural Network	—Unverified	0
Constructing Tree-based Index for Efficient and Effective Dense Retrieval	Apr 24, 2023	Contrastive LearningRetrieval	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