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

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Showing 1511–1520 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
Contrast, Stylize and Adapt: Unsupervised Contrastive Learning Framework for Domain Adaptive Semantic Segmentation	Jun 15, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	1	5
Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph Completion	Jul 3, 2024	Contrastive LearningKnowledge Graph Completion	CodeCode Available	1	5
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification	May 29, 2025	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation	Aug 27, 2022	Contrastive LearningRecommendation Systems	CodeCode Available	1	5
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection	Aug 18, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning	Mar 28, 2022	Action ClassificationContrastive Learning	CodeCode Available	1	5
Frequency-Based Alignment of EEG and Audio Signals Using Contrastive Learning and SincNet for Auditory Attention Detection	Mar 6, 2025	Contrastive LearningEEG	CodeCode Available	1	5
Frequency Enhanced Hybrid Attention Network for Sequential Recommendation	Apr 18, 2023	Contrastive LearningSequential Recommendation	CodeCode Available	1	5
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition	Apr 11, 2022	Contrastive LearningLow Resource Named Entity Recognition	CodeCode Available	1	5
Contrastive Domain Adaptation for Time-Series via Temporal Mixup	Dec 3, 2022	Contrastive LearningDomain Adaptation	CodeCode Available	1	5

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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