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 1101–1110 of 6661 papers

Title	Date	Tasks	Status	Hype
Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding	Oct 7, 2022	Contrastive LearningMedical Code Prediction	CodeCode Available	1
Uncovering the Structural Fairness in Graph Contrastive Learning	Oct 6, 2022	Contrastive LearningFairness	CodeCode Available	1
Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding	Oct 6, 2022	3D Object ClassificationContrastive Learning	CodeCode Available	1
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning	Oct 5, 2022	Active LearningContrastive Learning	CodeCode Available	1
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum	Oct 5, 2022	Contrastive Learning	CodeCode Available	1
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation	Oct 4, 2022	3D Semantic SegmentationContrastive Learning	CodeCode Available	1
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization	Oct 4, 2022	Contrastive LearningImage Retrieval	CodeCode Available	1
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training	Oct 3, 2022	3D Point Cloud ClassificationContrastive Learning	CodeCode Available	1
ContraCLM: Contrastive Learning For Causal Language Model	Oct 3, 2022	Code GenerationCode Search	CodeCode Available	1
Spectral Augmentation for Self-Supervised Learning on Graphs	Oct 2, 2022	Contrastive LearningNode Classification	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