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 5301–5350 of 6661 papers

TitleStatusHype
Your decision path does matter in pre-training industrial recommenders with multi-source behaviors—0
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning—0
Zero-CL: Instance and Feature decorrelation for negative-free symmetric contrastive learning—0
Fine-Tuned but Zero-Shot 3D Shape Sketch View Similarity and Retrieval—0
Zero-shot domain adaptation based on dual-level mix and contrast—0
Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures—0
Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning—0
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations—0
Adversarial Defense Framework for Graph Neural Network—0
Multi-Centroid Representation Network for Domain Adaptive Person Re-ID—0
Multi-Channel Hypergraph Contrastive Learning for Matrix Completion—0
Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation—0
Multi-Domain Features Guided Supervised Contrastive Learning for Radar Target Detection—0
Multi-Domain Learning From Insufficient Annotations—0
Multi-Domain Self-Supervised Learning—0
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
Multi-modal Causal Structure Learning and Root Cause Analysis—0
Multi-Modal CLIP-Informed Protein Editing—0
Multimodal Contrastive In-Context Learning—0
Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables—0
Multimodal Contrastive Learning and Tabular Attention for Automated Alzheimer's Disease Prediction—0
Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata—0
Multi-Modal Contrastive Learning for Online Clinical Time-Series Applications—0
Multimodal contrastive learning for remote sensing tasks—0
Multi-modal Contrastive Learning for Tumor-specific Missing Modality Synthesis—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—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