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 1281–1290 of 6661 papers

Title	Date	Tasks	Status	Hype
Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework	Apr 27, 2022	AllContrastive Learning	CodeCode Available	1
Self-Supervised Contrastive Learning for Singing Voices	Apr 26, 2022	Contrastive LearningSinger Identification	CodeCode Available	1
Hypergraph Contrastive Collaborative Filtering	Apr 26, 2022	Collaborative FilteringContrastive Learning	CodeCode Available	1
A Review-aware Graph Contrastive Learning Framework for Recommendation	Apr 26, 2022	Contrastive LearningRecommendation Systems	CodeCode Available	1
Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representation	Apr 26, 2022	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1
Contrastive Learning for Knowledge Tracing	Apr 25, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Pre-train a Discriminative Text Encoder for Dense Retrieval via Contrastive Span Prediction	Apr 22, 2022	Contrastive LearningDecoder	CodeCode Available	1
MCSE: Multimodal Contrastive Learning of Sentence Embeddings	Apr 22, 2022	Contrastive LearningSemantic Textual Similarity	CodeCode Available	1
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map	Apr 21, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
Contrastive Test-Time Adaptation	Apr 21, 2022	Contrastive LearningDomain Adaptation	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