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 621–630 of 6661 papers

Title	Date	Tasks	Status	Hype
Enhancing Multimodal Entity Linking with Jaccard Distance-based Conditional Contrastive Learning and Contextual Visual Augmentation	Jan 24, 2025	Contrastive LearningEntity Linking	—Unverified	0
MCRL4OR: Multimodal Contrastive Representation Learning for Off-Road Environmental Perception	Jan 23, 2025	Autonomous VehiclesContrastive Learning	CodeCode Available	0
Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation	Jan 23, 2025	Contrastive LearningOrgan Segmentation	CodeCode Available	1
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models	Jan 23, 2025	Backdoor AttackContrastive Learning	—Unverified	0
Solving the long-tailed distribution problem by exploiting the synergies and balance of different techniques	Jan 23, 2025	Contrastive Learning	—Unverified	0
MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender Systems	Jan 23, 2025	Contrastive LearningData Augmentation	CodeCode Available	1
Rethinking the Sample Relations for Few-Shot Classification	Jan 23, 2025	ClassificationContrastive Learning	CodeCode Available	7
2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings	Jan 23, 2025	Contrastive LearningSemantic Textual Similarity	—Unverified	0
Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer	Jan 23, 2025	ClassificationContrastive Learning	—Unverified	0
Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?	Jan 22, 2025	Contrastive LearningRetrieval	—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