SOTAVerified

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 2701–2725 of 6661 papers

TitleStatusHype
CATE Estimation With Potential Outcome Imputation From Local Regression—0
Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic RepresentationsCode1
Large Language Model based Long-tail Query Rewriting in Taobao SearchCode3
Can CLIP Help Sound Source Localization?Code1
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation—0
Multi-View Causal Representation Learning with Partial ObservabilityCode1
Temporal Graph Representation Learning with Adaptive Augmentation Contrastive—0
Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation—0
An Efficient Self-Supervised Cross-View Training For Sentence EmbeddingCode1
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
CycleCL: Self-supervised Learning for Periodic Videos—0
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
Contrastive Deep Nonnegative Matrix Factorization for Community DetectionCode1
FaMeSumm: Investigating and Improving Faithfulness of Medical SummarizationCode1
Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-trainingCode1
CheX-Nomaly: Segmenting Lung Abnormalities from Chest Radiographs using Machine Learning—0
SMORE: Score Models for Offline Goal-Conditioned Reinforcement Learning—0
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning—0
Contrastive Modules with Temporal Attention for Multi-Task Reinforcement LearningCode0
FLAP: Fast Language-Audio Pre-training—0
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification—0
Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image ClassificationCode0
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud RegistrationCode1
Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identificationCode0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified