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 1761–1770 of 6661 papers

Title	Date	Tasks	Status	Hype
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization	Oct 30, 2023	Contrastive LearningDiversity	CodeCode Available	1
Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning	Mar 14, 2022	AttributeContrastive Learning	CodeCode Available	1
Simplified Transfer Learning for Chest Radiography Models Using Less Data	Jul 19, 2022	Contrastive LearningTransfer Learning	CodeCode Available	1
SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization	Aug 31, 2022	Contrastive LearningFine-Grained Image Classification	CodeCode Available	1
Simple Contrastive Representation Learning for Time Series Forecasting	Mar 31, 2023	Contrastive LearningRepresentation Learning	CodeCode Available	1
Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning	Jan 1, 2021	3D Shape RetrievalContrastive Learning	CodeCode Available	1
Enhancing Modal Fusion by Alignment and Label Matching for Multimodal Emotion Recognition	Aug 18, 2024	Contrastive LearningEmotion Recognition	CodeCode Available	1
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning	Sep 10, 2021	Continual PretrainingContrastive Learning	CodeCode Available	1
Deep Contrastive One-Class Time Series Anomaly Detection	Jul 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning	Mar 4, 2024	Contrastive LearningCross-Domain Few-Shot	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