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 801–810 of 6661 papers

Title	Date	Tasks	Status	Hype
Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification	May 23, 2023	Audio ClassificationContrastive Learning	CodeCode Available	1
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions	May 23, 2023	Contrastive LearningImage-text Retrieval	CodeCode Available	1
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation	May 23, 2023	Contrastive LearningMachine Translation	CodeCode Available	1
Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering	May 23, 2023	ClusteringContrastive Learning	CodeCode Available	1
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings	May 23, 2023	Community DetectionContrastive Learning	CodeCode Available	1
On Learning to Summarize with Large Language Models as References	May 23, 2023	ArticlesContrastive Learning	CodeCode Available	1
Learning Emotion Representations from Verbal and Nonverbal Communication	May 22, 2023	Contrastive LearningEmotion Recognition	CodeCode Available	1
Open-world Semi-supervised Novel Class Discovery	May 22, 2023	Contrastive LearningNovel Class Discovery	CodeCode Available	1
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning	May 21, 2023	Abstract Meaning RepresentationContrastive Learning	CodeCode Available	1
Adaptive Graph Contrastive Learning for Recommendation	May 18, 2023	Collaborative FilteringContrastive Learning	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