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 4101–4110 of 6661 papers

Title	Date	Tasks	Status	Hype
Masked autoencoders are effective solution to transformer data-hungry	Dec 12, 2022	Contrastive LearningInductive Bias	CodeCode Available	1
ALSO: Automotive Lidar Self-supervision by Occupancy estimation	Dec 12, 2022	Autonomous DrivingContrastive Learning	CodeCode Available	1
Momentum Contrastive Pre-training for Question Answering	Dec 12, 2022	BenchmarkingContrastive Learning	—Unverified	0
Using Multiple Instance Learning to Build Multimodal Representations	Dec 11, 2022	Contrastive LearningCross-Modal Retrieval	—Unverified	0
Feature-Level Debiased Natural Language Understanding	Dec 11, 2022	Contrastive LearningNatural Language Understanding	CodeCode Available	0
Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense	Dec 11, 2022	Contrastive LearningRecommendation Systems	CodeCode Available	1
YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation	Dec 11, 2022	Contrastive LearningImage Generation	CodeCode Available	1
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition	Dec 11, 2022	Contrastive LearningGeneral Knowledge	CodeCode Available	0
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification	Dec 11, 2022	ClassificationContrastive Learning	CodeCode Available	1
MED-SE: Medical Entity Definition-based Sentence Embedding	Dec 9, 2022	Contrastive LearningSemantic Textual Similarity	—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