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 4726–4750 of 6661 papers

TitleStatusHype
Covidia: COVID-19 Interdisciplinary Academic Knowledge Graph—0
CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category DiscoveryCode0
Verbs in Action: Improving verb understanding in video-language modelsCode0
Robust Multiview Multimodal Driver Monitoring System Using Masked Multi-Head Self-AttentionCode0
CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data—0
RECLIP: Resource-efficient CLIP by Training with Small Images—0
Learning Transferable Pedestrian Representation from Multimodal Information SupervisionCode0
Looking Similar, Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning—0
Triple Sequence Learning for Cross-domain Recommendation—0
Semi-Supervised Relational Contrastive Learning—0
Sentence-Level Relation Extraction via Contrastive Learning with Descriptive Relation Prompts—0
Delving into E-Commerce Product Retrieval with Vision-Language Pre-training—0
Investigating Graph Structure Information for Entity Alignment with Dangling Cases—0
Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos—0
OpenDriver: An Open-Road Driver State Detection DatasetCode0
Continual Graph Convolutional Network for Text ClassificationCode0
The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning—0
Attack-Augmentation Mixing-Contrastive Skeletal Representation LearningCode0
Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images—0
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining—0
On the Importance of Contrastive Loss in Multimodal Learning—0
Supervised Contrastive Learning with Heterogeneous Similarity for Distribution Shifts—0
Masked Student Dataset of ExpressionsCode0
Linking Representations with Multimodal Contrastive Learning—0
Evidentiality-aware Retrieval for Overcoming Abstractiveness in Open-Domain Question Answering—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