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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4826–4850 of 6661 papers

TitleStatusHype
Extended Cross-Modality United Learning for Unsupervised Visible-Infrared Person Re-identification—0
Extending Contrastive Learning to Unsupervised Coreset Selection—0
External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
Extreme Multi-Label Skill Extraction Training using Large Language Models—0
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast—0
Face-to-Face Contrastive Learning for Social Intelligence Question-Answering—0
FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious disease—0
Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations—0
FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks—0
FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Prediction—0
FACTUAL: A Novel Framework for Contrastive Learning Based Robust SAR Image Classification—0
Factual Dialogue Summarization via Learning from Large Language Models—0
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning—0
FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Training—0
Fair Anomaly Detection For Imbalanced Groups—0
FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition—0
FaIRCoP: Facial Image Retrieval using Contrastive Personalization—0
FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis—0
FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records—0
FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders—0
Fairness-Aware Node Representation Learning—0
Fair Node Representation Learning via Adaptive Data Augmentation—0
Fair-VPT: Fair Visual Prompt Tuning for Image Classification—0
FakeReasoning: Towards Generalizable Forgery Detection and Reasoning—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