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 5751–5775 of 6661 papers

TitleStatusHype
Multi-Facet Counterfactual Learning for Content Quality Evaluation—0
Multi-Format Contrastive Learning of Audio Representations—0
Multi-Grained Cross-modal Alignment for Learning Open-vocabulary Semantic Segmentation from Text Supervision—0
Multi-granularity Item-based Contrastive Recommendation—0
Multi-head attention debiasing and contrastive learning for mitigating Dataset Artifacts in Natural Language Inference—0
Multi-hop Commonsense Knowledge Injection Framework for Zero-Shot Commonsense Question Answering—0
Multi-intent Aware Contrastive Learning for Sequential Recommendation—0
Multi-Label Image Classification with Contrastive Learning—0
Multi-Label Self-Supervised Learning with Scene Images—0
Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation—0
Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer—0
Multi-Level Contrastive Learning for Cross-Lingual Alignment—0
Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding—0
Multi-Level Contrastive Learning for Few-Shot Problems—0
Multi-level Contrastive Learning Framework for Sequential Recommendation—0
Multi-Level Graph Contrastive Learning—0
Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering—0
Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles—0
Multi-level Supervised Contrastive Learning—0
Multilingual Augmentation for Robust Visual Question Answering in Remote Sensing Images—0
Multilingual BERT Post-Pretraining Alignment—0
Multilingual Representation Distillation with Contrastive Learning—0
Multimodal 3D Object Detection on Unseen Domains—0
Multimodal and Contrastive Learning for Click Fraud Detection—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—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