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 31–40 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations	Apr 25, 2024	Contrastive LearningMusic Generation	CodeCode Available	3	5
Large Language Model based Long-tail Query Rewriting in Taobao Search	Nov 7, 2023	Contrastive LearningLanguage Modeling	CodeCode Available	3	5
Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection	Apr 22, 2025	Contrastive LearningFraud Detection	CodeCode Available	3	5
Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations	Oct 15, 2022	Contrastive LearningData Augmentation	CodeCode Available	3	5
A Survey on Self-Supervised Learning for Non-Sequential Tabular Data	Feb 2, 2024	Contrastive LearningDescriptive	CodeCode Available	3	5
FruitNeRF++: A Generalized Multi-Fruit Counting Method Utilizing Contrastive Learning and Neural Radiance Fields	May 26, 2025	Contrastive Learning	CodeCode Available	3	5
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment	Dec 1, 2023	Contrastive LearningFew-Shot Learning	CodeCode Available	3	5
Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion	Dec 5, 2024	Contrastive LearningHallucination	CodeCode Available	3	5
Focused Transformer: Contrastive Training for Context Scaling	Jul 6, 2023	Contrastive Learning	CodeCode Available	3	5
Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling	Jan 9, 2023	2D Object DetectionContrastive Learning	CodeCode Available	3	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