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 2951–2975 of 6661 papers

TitleStatusHype
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node TasksCode1
Self-supervised Multi-view Clustering in Computer Vision: A Survey—0
Traffic Scene Similarity: a Graph-based Contrastive Learning Approach—0
Towards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective—0
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight—0
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection—0
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery CluesCode1
MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision TransformerCode1
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding—0
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels—0
DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions—0
A Generative Framework for Self-Supervised Facial Representation LearningCode0
Supervised Stochastic Neighbor Embedding Using Contrastive LearningCode0
CvFormer: Cross-view transFormers with Pre-training for fMRI Analysis of Human Brain—0
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax EntropyCode0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
Road Disease Detection based on Latent Domain Background Feature Separation and Suppression—0
DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective—0
Hodge-Aware Contrastive Learning—0
Domain-Aware Augmentations for Unsupervised Online General Continual Learning—0
Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning—0
Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal ContrastCode1
Multi-behavior Recommendation with SVD Graph Neural Networks—0
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive LearningCode1
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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