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 2171–2180 of 6661 papers

Title	Date	Tasks	Status	Hype
BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learning	Mar 4, 2024	Contrastive Learning	—Unverified	0
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion	Mar 4, 2024	Contrastive LearningDrug Design	—Unverified	0
NoteLLM: A Retrievable Large Language Model for Note Recommendation	Mar 4, 2024	Contrastive LearningLanguage Modeling	—Unverified	0
A Theoretical Analysis of Self-Supervised Learning for Vision Transformers	Mar 4, 2024	Contrastive LearningPosition	—Unverified	0
Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images	Mar 4, 2024	ClusteringContrastive Learning	CodeCode Available	0
Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents	Mar 4, 2024	Contrastive Learning	CodeCode Available	3
Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity	Mar 4, 2024	Contrastive LearningEnsemble Learning	—Unverified	0
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning	Mar 4, 2024	Contrastive LearningCross-Domain Few-Shot	CodeCode Available	1
Partial Federated Learning	Mar 3, 2024	Contrastive LearningFederated Learning	—Unverified	0
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network	Mar 3, 2024	Binary ClassificationContrastive Learning	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