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 311–320 of 6661 papers

Title	Date	Tasks	Status	Hype
Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object Detection	Apr 9, 2025	Contrastive Learningcounterfactual	CodeCode Available	2
GOLLuM: Gaussian Process Optimized LLMs -- Reframing LLM Finetuning through Bayesian Optimization	Apr 8, 2025	Bayesian OptimizationContrastive Learning	CodeCode Available	1
Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation	Apr 8, 2025	Collaborative FilteringContrastive Learning	CodeCode Available	1
Large Language Models Enhanced Hyperbolic Space Recommender Systems	Apr 8, 2025	Contrastive LearningRecommendation Systems	—Unverified	0
Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation	Apr 8, 2025	Contrastive LearningRepresentation Learning	—Unverified	0
MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image Segmentation	Apr 7, 2025	Contrastive LearningDiagnostic	CodeCode Available	0
TC-MGC: Text-Conditioned Multi-Grained Contrastive Learning for Text-Video Retrieval	Apr 7, 2025	Contrastive LearningRetrieval	CodeCode Available	0
Sub-Clustering for Class Distance Recalculation in Long-Tailed Drug Classification	Apr 7, 2025	Contrastive Learning	—Unverified	0
Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering	Apr 6, 2025	Collaborative FilteringContrastive Learning	CodeCode Available	0
QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors	Apr 5, 2025	Contrastive LearningRAG	—Unverified	0

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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