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 1521–1530 of 6661 papers

Title	Date	Tasks	Status	Hype
AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators	Sep 21, 2021	Contrastive LearningGraph Representation Learning	CodeCode Available	1
On the Importance of Distractors for Few-Shot Classification	Sep 20, 2021	ClassificationContrastive Learning	CodeCode Available	1
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models	Sep 19, 2021	Contextualised Word RepresentationsContrastive Learning	CodeCode Available	1
Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck	Sep 17, 2021	Contrastive LearningRepresentation Learning	CodeCode Available	1
Self-supervised Contrastive Learning for EEG-based Sleep Staging	Sep 16, 2021	Contrastive LearningEEG	CodeCode Available	1
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations	Sep 15, 2021	CoLAContrastive Learning	CodeCode Available	1
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning	Sep 15, 2021	Contrastive LearningFew-shot NER	CodeCode Available	1
Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation	Sep 15, 2021	Contrastive LearningImage Segmentation	CodeCode Available	1
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning	Sep 13, 2021	Contrastive LearningIntent Detection	CodeCode Available	1
Exploring Task Difficulty for Few-Shot Relation Extraction	Sep 12, 2021	Contrastive LearningMeta-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