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 6276–6300 of 6661 papers

TitleStatusHype
Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment—0
Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation—0
Scaling Law for Recommendation Models: Towards General-purpose User Representations—0
Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization—0
SCALP -- Supervised Contrastive Learning for Cardiopulmonary Disease Classification and Localization in Chest X-rays using Patient Metadata—0
SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption—0
SCAT: Robust Self-supervised Contrastive Learning via Adversarial Training for Text Classification—0
SCDNet: Self-supervised Learning Feature-based Speaker Change Detection—0
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition—0
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation—0
Scene Graph Contrastive Learning for Embodied Navigation—0
Scene Graph Embeddings Using Relative Similarity Supervision—0
SCFCRC: Simultaneously Counteract Feature Camouflage and Relation Camouflage for Fraud Detection—0
SCHEMA: State CHangEs MAtter for Procedure Planning in Instructional Videos—0
scMamba: A Scalable Foundation Model for Single-Cell Multi-Omics Integration Beyond Highly Variable Feature Selection—0
SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology—0
SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions—0
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation—0
ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function—0
Score Combining for Contrastive OOD Detection—0
SMORE: Score Models for Offline Goal-Conditioned Reinforcement Learning—0
SCoRe: Submodular Combinatorial Representation Learning—0
SCOTCH and SODA: A Transformer Video Shadow Detection Framework—0
Scrutinizing Shipment Records To Thwart Illegal Timber Trade—0
SCVRL: Shuffled Contrastive Video Representation Learning—0
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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