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 4451–4460 of 6661 papers

Title	Date	Tasks	Status	Hype
Contrastive Audio-Visual Masked Autoencoder	Oct 2, 2022	Audio ClassificationAudio Tagging	CodeCode Available	2
Fine-grained Contrastive Learning for Definition Generation	Oct 2, 2022	Contrastive LearningDecoder	CodeCode Available	0
Spectral Augmentation for Self-Supervised Learning on Graphs	Oct 2, 2022	Contrastive LearningNode Classification	CodeCode Available	1
Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing	Oct 1, 2022	Answer SelectionContrastive Learning	CodeCode Available	0
Improving Deep Embedded Clustering via Learning Cluster-level Representations	Oct 1, 2022	ClusteringContrastive Learning	—Unverified	0
Generalizable Implicit Hate Speech Detection Using Contrastive Learning	Oct 1, 2022	Contrastive LearningHate Speech Detection	CodeCode Available	1
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis	Oct 1, 2022	Contrastive LearningMultimodal Sentiment Analysis	—Unverified	0
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction	Oct 1, 2022	Contrastive LearningMeta-Learning	—Unverified	0
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition	Oct 1, 2022	Contrastive Learningfew-shot-ner	CodeCode Available	1
Table-based Fact Verification with Self-labeled Keypoint Alignment	Oct 1, 2022	AttributeContrastive Learning	—Unverified	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