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 1261–1270 of 6661 papers

Title	Date	Tasks	Status	Hype
Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization	May 15, 2022	Contrastive LearningIntent Detection	CodeCode Available	1
Near out-of-distribution detection for low-resolution radar micro-Doppler signatures	May 12, 2022	Contrastive LearningGeometry-aware processing	CodeCode Available	1
Multiplexed Immunofluorescence Brain Image Analysis Using Self-Supervised Dual-Loss Adaptive Masked Autoencoder	May 10, 2022	Cell DetectionContrastive Learning	CodeCode Available	1
Learning Non-target Knowledge for Few-shot Semantic Segmentation	May 10, 2022	Contrastive LearningFew-Shot Semantic Segmentation	CodeCode Available	1
EASE: Entity-Aware Contrastive Learning of Sentence Embedding	May 9, 2022	ClusteringContrastive Learning	CodeCode Available	1
A Simple Contrastive Learning Objective for Alleviating Neural Text Degeneration	May 5, 2022	Contrastive LearningDialogue Generation	CodeCode Available	1
Cross-modal Contrastive Learning for Speech Translation	May 5, 2022	Contrastive LearningRetrieval	CodeCode Available	1
UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning	May 4, 2022	Contrastive LearningImage Dehazing	CodeCode Available	1
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction	May 4, 2022	ClusteringContrastive Learning	CodeCode Available	1
Contrastive Learning for Prompt-Based Few-Shot Language Learners	May 3, 2022	Contrastive LearningIn-Context 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