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 5376–5400 of 6661 papers

TitleStatusHype
KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning—0
KDC-MAE: Knowledge Distilled Contrastive Mask Auto-Encoder—0
K-Diag: Knowledge-enhanced Disease Diagnosis in Radiographic Imaging—0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering—0
Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images—0
TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance—0
Keyword-Based Diverse Image Retrieval by Semantics-aware Contrastive Learning and Transformer—0
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation—0
KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning—0
KFCNet: Knowledge Filtering and Contrastive Learning for Generative Commonsense Reasoning—0
Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives—0
Enhancing CLIP Conceptual Embedding through Knowledge Distillation—0
KNN-BERT: Fine-Tuning Pre-Trained Models with KNN Classifier—0
KNN-Contrastive Learning for Out-of-Domain Intent Classification—0
KnowAugNet: Multi-Source Medical Knowledge Augmented Medication Prediction Network with Multi-Level Graph Contrastive Learning—0
Knowledge-aware contrastive heterogeneous molecular graph learning—0
Knowledge-aware Contrastive Molecular Graph Learning—0
Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation—0
Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning—0
Knowledge-Driven Self-Supervised Representation Learning for Facial Action Unit Recognition—0
Knowledge Enhancement for Contrastive Multi-Behavior Recommendation—0
Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation—0
Knowledge graph-enhanced molecular contrastive learning with functional prompt—0
Knowledge-Rich Self-Supervision for Biomedical Entity Linking—0
K-Shot Contrastive Learning of Visual Features with Multiple Instance Augmentations—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