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 2751–2760 of 6661 papers

Title	Date	Tasks	Status	Hype
Spatio-Temporal Meta Contrastive Learning	Oct 26, 2023	Contrastive LearningCrime Prediction	CodeCode Available	1
Image Prior and Posterior Conditional Probability Representation for Efficient Damage Assessment	Oct 26, 2023	Computational EfficiencyContrastive Learning	—Unverified	0
Boosting Multi-Speaker Expressive Speech Synthesis with Semi-supervised Contrastive Learning	Oct 26, 2023	Contrastive LearningExpressive Speech Synthesis	—Unverified	0
Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification	Oct 26, 2023	Contrastive LearningPerson Re-Identification	CodeCode Available	1
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks	Oct 26, 2023	Contrastive LearningGraph Classification	CodeCode Available	0
SSLCL: An Efficient Model-Agnostic Supervised Contrastive Learning Framework for Emotion Recognition in Conversations	Oct 25, 2023	Contrastive LearningEmotion Recognition	CodeCode Available	1
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery	Oct 25, 2023	Contrastive Learningintent-classification	—Unverified	0
Learning Robust Deep Visual Representations from EEG Brain Recordings	Oct 25, 2023	Contrastive LearningEEG	CodeCode Available	1
Proposal-Contrastive Pretraining for Object Detection from Fewer Data	Oct 25, 2023	Contrastive LearningObject	—Unverified	0
Modality-Agnostic Self-Supervised Learning with Meta-Learned Masked Auto-Encoder	Oct 25, 2023	Contrastive LearningMeta-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