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 1976–2000 of 6661 papers

TitleStatusHype
CoCGAN: Contrastive Learning for Adversarial Category Text Generation—0
Domain Contrast for Domain Adaptive Object Detection—0
Feedback Reciprocal Graph Collaborative Filtering—0
Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations—0
Domain Generalization for Mammographic Image Analysis with Contrastive Learning—0
Divide and Contrast: Self-supervised Learning from Uncurated Data—0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
Coarse-to-Fine Contrastive Learning on Graphs—0
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images—0
Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality—0
ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering—0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
Domain Prompt Learning with Quaternion Networks—0
Decentralized Unsupervised Learning of Visual Representations—0
Don't Click the Bait: Title Debiasing News Recommendation via Cross-Field Contrastive Learning—0
Audio Contrastive based Fine-tuning—0
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning—0
Distributed Contrastive Learning for Medical Image Segmentation—0
Double Banking on Knowledge: Customized Modulation and Prototypes for Multi-Modality Semi-supervised Medical Image Segmentation—0
Distortion-Disentangled Contrastive Learning—0
FastCAD: Real-Time CAD Retrieval and Alignment from Scans and Videos—0
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method—0
DPCL-Diff: The Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning—0
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite—0
Distilling Structured Knowledge for Text-Based Relational 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