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 3271–3280 of 6661 papers

Title	Date	Tasks	Status
Learning Monolingual Sentence Embeddings with Large-scale Parallel Translation Datasets	Jan 16, 2022	Contrastive LearningSemantic Textual Similarity	—Unverified
ACC-ViT : Atrous Convolution's Comeback in Vision Transformers	Mar 7, 2024	Contrastive LearningMedical Image Analysis	—Unverified
Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition	Jun 1, 2025	Contrastive LearningEmotion Recognition	—Unverified
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation	Jul 13, 2023	Action RecognitionContrastive Learning	—Unverified
Learning on Graphs under Label Noise	Jun 14, 2023	Anomaly DetectionContrastive Learning	—Unverified
Boosting Knowledge Graph-based Recommendations through Confidence-Aware Augmentation with Large Language Models	Feb 6, 2025	Contrastive LearningExplanation Generation	—Unverified
Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning	Feb 24, 2022	Contrastive LearningData Augmentation	—Unverified
Mixed Graph Contrastive Network for Semi-Supervised Node Classification	Jun 6, 2022	ClassificationContrastive Learning	—Unverified
Frequency-Aware Contrastive Learning for Neural Machine Translation	Dec 29, 2021	Contrastive LearningDiversity	—Unverified
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier	Nov 21, 2022	AllContrastive Learning	—Unverified

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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