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 4521–4530 of 6661 papers

Title	Date	Tasks	Status	Hype
Statement-Level Vulnerability Detection: Learning Vulnerability Patterns Through Information Theory and Contrastive Learning	Sep 20, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	0
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings	Sep 20, 2022	ClusteringContrastive Learning	CodeCode Available	1
Learning Decoupled Retrieval Representation for Nearest Neighbour Neural Machine Translation	Sep 19, 2022	Contrastive LearningDecoder	—Unverified	0
Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction	Sep 19, 2022	Argument MiningContrastive Learning	—Unverified	0
S^3R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification	Sep 19, 2022	Contrastive Learningimage-classification	—Unverified	0
Mutual Harmony: Sequential Recommendation with Dual Contrastive Network	Sep 18, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	0
Few-Shot Classification with Contrastive Learning	Sep 17, 2022	ClassificationContrastive Learning	—Unverified	0
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence	Sep 16, 2022	Contrastive Learning	CodeCode Available	1
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning	Sep 16, 2022	Contrastive LearningMeta-Learning	CodeCode Available	1
Graph Contrastive Learning with Cross-view Reconstruction	Sep 16, 2022	Contrastive LearningDisentanglement	—Unverified	0

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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