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 10761100 of 6661 papers

TitleStatusHype
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment AnalysisCode1
Unsupervised visualization of image datasets using contrastive learningCode1
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive LearningCode1
Unifying Graph Contrastive Learning with Flexible Contextual ScopesCode1
Supervised Prototypical Contrastive Learning for Emotion Recognition in ConversationCode1
Towards Effective Image Manipulation Detection with Proposal Contrastive LearningCode1
How Mask Matters: Towards Theoretical Understandings of Masked AutoencodersCode1
Augmented Dual-Contrastive Aggregation Learning for Unsupervised Visible-Infrared Person Re-IdentificationCode1
Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive LearningCode1
MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge RepresentationCode1
Low-resource Neural Machine Translation with Cross-modal AlignmentCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Self-Attention Message Passing for Contrastive Few-Shot LearningCode1
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation LearningCode1
MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training ModelCode1
Contrastive Trajectory Similarity Learning with Dual-Feature AttentionCode1
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural NetworksCode1
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model PretrainingCode1
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link PredictionCode1
Towards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive LearningCode1
Contrastive Bayesian Analysis for Deep Metric LearningCode1
Revisiting Self-Supervised Contrastive Learning for Facial Expression RecognitionCode1
Hierarchical Few-Shot Object Detection: Problem, Benchmark and MethodCode1
InfoCSE: Information-aggregated Contrastive Learning of Sentence EmbeddingsCode1
Augmentations in Hypergraph Contrastive Learning: Fabricated and GenerativeCode1
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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