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 3826–3850 of 6661 papers

TitleStatusHype
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning—0
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU—0
A two-steps approach to improve the performance of Android malware detectors—0
Audio Contrastive based Fine-tuning—0
Self-supervised Contrastive Learning for Audio-Visual Action Recognition—0
Audio-Visual Contrastive Learning with Temporal Self-Supervision—0
Augmentation adversarial training for self-supervised speaker recognition—0
Augmentation-Free Graph Contrastive Learning with Performance Guarantee—0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
Augmented Contrastive Self-Supervised Learning for Audio Invariant Representations—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
A Unified and Efficient Contrastive Learning Framework for Text Summarization—0
A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training—0
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection—0
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix—0
Unified Framework for Feature Extraction based on Contrastive Learning—0
A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognition—0
A Unified Two-Stage Group Semantics Propagation and Contrastive Learning Network for Co-Saliency Detection—0
Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation—0
A Unique Training Strategy to Enhance Language Models Capabilities for Health Mention Detection from Social Media Content—0
AuthGuard: Generalizable Deepfake Detection via Language Guidance—0
Auto-Focus Contrastive Learning for Image Manipulation Detection—0
AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing—0
Automated Contrastive Learning Strategy Search for Time Series—0
Automated Radiology Report Generation: A Review of Recent Advances—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