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 5401–5425 of 6661 papers

TitleStatusHype
LaB-CL: Localized and Balanced Contrastive Learning for improving parking slot detection—0
Label Anchored Contrastive Learning for Language Understanding—0
Label-efficient Contrastive Learning-based model for nuclei detection and classification in 3D Cardiovascular Immunofluorescent Images—0
Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Contrastive Learning—0
Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection—0
Label-free Prediction of Vascular Connectivity in Perfused Microvascular Networks in vitro—0
Label-invariant Augmentation for Semi-Supervised Graph Classification—0
Label-noise-tolerant medical image classification via self-attention and self-supervised learning—0
LabelPrompt: Effective Prompt-based Learning for Relation Classification—0
Label-template based Few-Shot Text Classification with Contrastive Learning—0
LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space—0
LAC: Latent Action Composition for Skeleton-based Action Segmentation—0
LAC - Latent Action Composition for Skeleton-based Action Segmentation—0
LAMP: Learnable Meta-Path Guided Adversarial Contrastive Learning for Heterogeneous Graphs—0
Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning—0
Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification—0
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning—0
Language-guided Image Reflection Separation—0
Language-Inspired Relation Transfer for Few-shot Class-Incremental Learning—0
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks—0
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving—0
Large Language Model-Aware In-Context Learning for Code Generation—0
Large Language Model Meets Graph Neural Network in Knowledge Distillation—0
Large Language Models Enhanced Hyperbolic Space Recommender Systems—0
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping—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