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 5726–5750 of 6661 papers

TitleStatusHype
Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation—0
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning—0
Representation Disentanglement in Generative Models with Contrastive Learning—0
Federated Contrastive Learning for Privacy-Preserving Unpaired Image-to-Image Translation—0
Residual Contrastive Learning: Unsupervised Representation Learning from Residuals—0
Prototypical Contrastive Predictive Coding—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—0
Not All Regions are Worthy to be Distilled: Region-aware Knowledge Distillation Towards Efficient Image-to-Image Translation—0
Unsupervised Contrastive Learning for Signal-Dependent Noise Synthesis—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
Anomaly Detection for Tabular Data with Internal Contrastive Learning—0
Zero-CL: Instance and Feature decorrelation for negative-free symmetric contrastive learning—0
Hierarchical Cross Contrastive Learning of Visual Representations—0
Contrastively Enforcing Distinctiveness for Multi-Label Classification—0
S^3ADNet: Sequential Anomaly Detection with Pessimistic Contrastive Learning—0
What Makes for Good Representations for Contrastive Learning—0
Self-supervised Learning for Sequential Recommendation with Model Augmentation—0
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning—0
Contrastive Mutual Information Maximization for Binary Neural Networks—0
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning—0
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching—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