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 6501–6525 of 6661 papers

TitleStatusHype
PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning—0
Addressing Feature Suppression in Unsupervised Visual Representations—0
Understanding the Behaviour of Contrastive Loss—0
Wasserstein Contrastive Representation Distillation—0
LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding—0
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification—0
Contrastive Learning for Label-Efficient Semantic Segmentation—0
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering—0
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models—0
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning—0
Multi-Label Contrastive Learning for Abstract Visual ReasoningCode0
About contrastive unsupervised representation learning for classification and its convergence—0
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization—0
A Risk Communication Event Detection Model via Contrastive Learning—0
Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer's Disease Detection—0
Unsupervised Representation Learning by Invariance Propagation—0
Self-Supervised Relationship Probing—0
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding—0
Annotation-Efficient Untrimmed Video Action Recognition—0
PCPs: Patient Cardiac Prototypes—0
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age—0
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space—0
StackMix: A complementary Mix algorithm—0
Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections—0
Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised 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