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 5051–5075 of 6661 papers

TitleStatusHype
Mitigating Negative Style Transfer in Hybrid Dialogue SystemCode0
Establishing a stronger baseline for lightweight contrastive modelsCode0
Tailoring Visual Object Representations to Human Requirements: A Case Study with a Recycling RobotCode0
Boosting Semi-Supervised Learning with Contrastive Complementary Labeling—0
A Machine Learning Enhanced Approach for Automated Sunquake Detection in Acoustic Emission Maps—0
Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits—0
Coarse-to-Fine Contrastive Learning on Graphs—0
Momentum Contrastive Pre-training for Question Answering—0
Feature-Level Debiased Natural Language UnderstandingCode0
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor DecompositionCode0
Using Multiple Instance Learning to Build Multimodal Representations—0
Self-Supervised Object Goal Navigation with In-Situ Finetuning—0
Contrastive View Design Strategies to Enhance Robustness to Domain Shifts in Downstream Object Detection—0
MED-SE: Medical Entity Definition-based Sentence Embedding—0
Localized Contrastive Learning on Graphs—0
SimVTP: Simple Video Text Pre-training with Masked AutoencodersCode0
Unsupervised Flood Detection on SAR Time Series—0
GraphLearner: Graph Node Clustering with Fully Learnable AugmentationCode0
Semi-Supervised Object Detection with Object-wise Contrastive Learning and Regression Uncertainty—0
Self-supervised and Weakly Supervised Contrastive Learning for Frame-wise Action Representations—0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation—0
Location-Aware Self-Supervised Transformers for Semantic Segmentation—0
Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning—0
Few-Shot Nested Named Entity Recognition—0
Spectral Feature Augmentation for Graph Contrastive Learning and Beyond—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