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 6576–6600 of 6661 papers

TitleStatusHype
Function Contrastive Learning of Transferable Representations—0
In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object LocalizationCode0
Structure Aware Negative Sampling in Knowledge Graphs—0
Feature Distillation With Guided Adversarial Contrastive Learning—0
Enhancing Dialogue Generation via Multi-Level Contrastive Learning—0
The Next Big Thing(s) in Unsupervised Machine Learning: Five Lessons from Infant Learning—0
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent LossCode0
Self-supervised pre-training and contrastive representation learning for multiple-choice video QA—0
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations—0
SAIL: Self-Augmented Graph Contrastive Learning—0
Learning Node Representations against PerturbationsCode0
Contrastive learning, multi-view redundancy, and linear models—0
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics—0
Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound—0
What Should Not Be Contrastive in Contrastive Learning—0
Data Cleansing with Contrastive Learning for Vocal Note Event AnnotationsCode0
LoCo: Local Contrastive Representation Learning—0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
A Visual Analytics Framework for Contrastive Network Analysis—0
On Learning Universal Representations Across Languages—0
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction—0
K-Shot Contrastive Learning of Visual Features with Multiple Instance Augmentations—0
Contrastive Visual-Linguistic PretrainingCode0
Gradient Regularized Contrastive Learning for Continual Domain Adaptation—0
Few-shot Visual Reasoning with Meta-analogical Contrastive 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