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 6101–6125 of 6661 papers

TitleStatusHype
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—0
The Power of Contrast for Feature Learning: A Theoretical Analysis—0
Contrastive Learning for Unsupervised Radar Place Recognition—0
Cut the CARP: Fishing for zero-shot story evaluation—0
Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime PredictionCode0
SeanNet: Semantic Understanding Network for Localization Under Object DynamicsCode0
Deep Fraud Detection on Non-attributed Graph—0
Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge—0
Stochastic Contrastive Learning—0
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations—0
Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation—0
Key Point Analysis via Contrastive Learning and Extractive Argument SummarizationCode0
Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views—0
The Details Matter: Preventing Class Collapse in Supervised Contrastive Learning—0
A Rate-Distortion Approach to Domain Generalization—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Approximate Bijective Correspondence for isolating factors of variationCode0
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning—0
Anomaly Detection for Tabular Data with Internal Contrastive Learning—0
Prototypical Contrastive Predictive Coding—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
Data-Efficient Contrastive Learning by Differentiable Hard Sample and Hard Positive Pair Generation—0
Understanding Self-supervised Learning via Information Bottleneck Principle—0
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—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