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 4611–4620 of 6661 papers

Title	Date	Tasks	Status	Hype
FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning	Aug 22, 2022	AttributeContrastive Learning	CodeCode Available	1
TaCo: Textual Attribute Recognition via Contrastive Learning	Aug 22, 2022	AttributeContrastive Learning	—Unverified	0
Patient-level Microsatellite Stability Assessment from Whole Slide Images By Combining Momentum Contrast Learning and Group Patch Embeddings	Aug 22, 2022	ClassificationContrastive Learning	CodeCode Available	0
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations	Aug 21, 2022	Contrastive LearningMultimodal Sentiment Analysis	—Unverified	0
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization	Aug 21, 2022	Contrastive LearningExtractive Summarization	—Unverified	0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval	Aug 21, 2022	ClusteringContrastive Learning	—Unverified	0
Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound	Aug 21, 2022	Contrastive Learning	—Unverified	0
Fuse and Attend: Generalized Embedding Learning for Art and Sketches	Aug 20, 2022	Contrastive LearningImage Retrieval	—Unverified	0
GraphTTA: Test Time Adaptation on Graph Neural Networks	Aug 19, 2022	Contrastive LearningTest-time Adaptation	—Unverified	0
MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation	Aug 18, 2022	Contrastive LearningSegmentation	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