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 1701–1710 of 6661 papers

Title	Date	Tasks	Status	Hype
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World Attacks	Jun 9, 2024	Adversarial RobustnessAutonomous Driving	CodeCode Available	1
A DeNoising FPN With Transformer R-CNN for Tiny Object Detection	Jun 9, 2024	Contrastive LearningDenoising	CodeCode Available	2
Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language	Jun 9, 2024	Contrastive LearningCross-Modal Retrieval	CodeCode Available	2
Advancing Semantic Textual Similarity Modeling: A Regression Framework with Translated ReLU and Smooth K2 Loss	Jun 8, 2024	Contrastive LearningSemantic Textual Similarity	CodeCode Available	0
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models	Jun 8, 2024	Contrastive Learning	CodeCode Available	1
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning	Jun 7, 2024	Contrastive LearningLanguage Modeling	CodeCode Available	1
CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment	Jun 7, 2024	Contrastive LearningZero-Shot Learning	CodeCode Available	1
QAGCF: Graph Collaborative Filtering for Q&A Recommendation	Jun 7, 2024	Collaborative FilteringContrastive Learning	—Unverified	0
Skill-aware Mutual Information Optimisation for Generalisation in Reinforcement Learning	Jun 7, 2024	Contrastive LearningMeta Reinforcement Learning	CodeCode Available	1
Denoising-Aware Contrastive Learning for Noisy Time Series	Jun 7, 2024	Contrastive LearningDenoising	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