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 3126–3150 of 6661 papers

TitleStatusHype
Source-free Domain Adaptive Human Pose EstimationCode1
Semi-supervised Contrastive Regression for Estimation of Eye Gaze—0
One-stage Low-resolution Text Recognition with High-resolution Knowledge TransferCode1
Bootstrapping Contrastive Learning Enhanced Music Cold-Start Matching—0
From Fake to Hyperpartisan News Detection Using Domain Adaptation—0
Learning Referring Video Object Segmentation from Weak Annotation—0
Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive LearningCode0
Adapting to Change: Robust Counterfactual Explanations in Dynamic Data LandscapesCode0
Learning beyond sensations: how dreams organize neuronal representations—0
COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery DetectionCode0
Unsupervised Representation Learning for Time Series: A ReviewCode1
Orientation-Guided Contrastive Learning for UAV-View Geo-Localisation—0
A Multi-Source Data Fusion-based Semantic Segmentation Model for Relic Landslide Detection—0
Three Factors to Improve Out-of-Distribution Detection—0
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach—0
ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic CorporaCode0
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders—0
Dynamically Scaled Temperature in Self-Supervised Contrastive LearningCode0
Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item RetrievalCode0
Center Contrastive Loss for Metric Learning—0
Graph Embedding Dynamic Feature-based Supervised Contrastive Learning of Transient Stability for Changing Power Grid Topologies—0
Graph Contrastive Learning with Generative Adversarial Network—0
Relational Contrastive Learning for Scene Text RecognitionCode1
Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?Code0
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent MaskingCode1
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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