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 3421–3430 of 6661 papers

Title	Date	Tasks	Status	Hype
Temporal Contrastive Learning for Spiking Neural Networks	May 23, 2023	Contrastive Learning	—Unverified	0
SiCL: Silhouette-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes Change	May 23, 2023	Clothes Changing Person Re-IdentificationContrastive Learning	CodeCode Available	1
Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training	May 23, 2023	Contrastive LearningSelf-Supervised Learning	CodeCode Available	5
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs	May 23, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification	May 23, 2023	Audio ClassificationContrastive Learning	CodeCode Available	1
Generalizable Synthetic Image Detection via Language-guided Contrastive Learning	May 23, 2023	Contrastive LearningImage Generation	CodeCode Available	1
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality	May 23, 2023	AttributeContrastive Learning	—Unverified	0
Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding	May 23, 2023	Citation PredictionContrastive Learning	—Unverified	0
Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path	May 23, 2023	Contrastive LearningKey-value Pair Extraction	—Unverified	0
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation	May 23, 2023	Contrastive LearningMachine Translation	CodeCode Available	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