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

Showing 47514775 of 6661 papers

TitleStatusHype
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image SegmentationCode0
Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
Block-SCL: Blocking Matters for Supervised Contrastive Learning in Product Matching0
Multi-Modal Multi-Correlation Learning for Audio-Visual Speech Separation0
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed RecognitionCode1
Game State Learning via Game Scene Augmentation0
Positive-Negative Equal Contrastive Loss for Semantic Segmentation0
Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasetsCode1
Multi-granularity Item-based Contrastive Recommendation0
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly DetectionCode1
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
Dynamic Contrastive Distillation for Image-Text Retrieval0
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval0
GUIM -- General User and Item Embedding with Mixture of Representation in E-commerce0
Contrastive Data and Learning for Natural Language Processing0
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection0
RCL: Relation Contrastive Learning for Zero-Shot Relation ExtractionCode0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling0
Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity RecognitionCode0
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations0
Exploring Contrastive Learning for Multimodal Detection of Misogynistic MemesCode2
PALI at SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts0
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified