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 1351–1360 of 6661 papers

Title	Date	Tasks	Status	Hype
Improving Word Translation via Two-Stage Contrastive Learning	Mar 15, 2022	Bilingual Lexicon InductionContrastive Learning	CodeCode Available	1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost	Mar 15, 2022	Contrastive LearningLanguage Modeling	CodeCode Available	1
S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning	Mar 14, 2022	Contrastive Learning	CodeCode Available	1
Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning	Mar 14, 2022	AttributeContrastive Learning	CodeCode Available	1
Rethinking Minimal Sufficient Representation in Contrastive Learning	Mar 14, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings	Mar 11, 2022	Contrastive LearningSemantic Textual Similarity	CodeCode Available	1
LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval	Mar 11, 2022	Contrastive LearningRe-Ranking	CodeCode Available	1
Neuromorphic Data Augmentation for Training Spiking Neural Networks	Mar 11, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Democracy Does Matter: Comprehensive Feature Mining for Co-Salient Object Detection	Mar 11, 2022	Contrastive LearningCo-Salient Object Detection	CodeCode Available	1
MetAug: Contrastive Learning via Meta Feature Augmentation	Mar 10, 2022	Contrastive LearningInformativeness	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