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 1351–1400 of 6661 papers

TitleStatusHype
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive LearningCode1
Dyadic Interaction Modeling for Social Behavior GenerationCode1
Dynamic Conceptional Contrastive Learning for Generalized Category DiscoveryCode1
Contrastive Learning of Generalized Game RepresentationsCode1
Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report GenerationCode1
Contrastive learning of global and local features for medical image segmentation with limited annotationsCode1
Contrastive Learning of Global-Local Video RepresentationsCode1
Mixup Your Own PairsCode1
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial LearningCode1
Contrastive Learning of Medical Visual Representations from Paired Images and TextCode1
Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive LearningCode1
E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time SeriesCode1
Compressive Visual RepresentationsCode1
Contrastive Multiview CodingCode1
UMETTS: A Unified Framework for Emotional Text-to-Speech Synthesis with Multimodal PromptsCode1
MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular GraphCode1
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and ClusteringCode1
Contrastive Learning of Sentence Embeddings from ScratchCode1
An Interactive Multi-modal Query Answering System with Retrieval-Augmented Large Language ModelsCode1
ECL: Class-Enhancement Contrastive Learning for Long-tailed Skin Lesion ClassificationCode1
Edge Guided GANs with Contrastive Learning for Semantic Image SynthesisCode1
Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image SynthesisCode1
Effective Conditioned and Composed Image Retrieval Combining CLIP-Based FeaturesCode1
EEG-CLIP : Learning EEG representations from natural language descriptionsCode1
Contrastive Learning of User Behavior Sequence for Context-Aware Document RankingCode1
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum LearningCode1
Modulated Contrast for Versatile Image SynthesisCode1
MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal AdapterCode1
Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp)Code1
Contrastive Learning Reduces Hallucination in ConversationsCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt VerbalizerCode1
Efficient Medical Vision-Language Alignment Through Adapting Masked Vision ModelsCode1
Efficient Non-Local Contrastive Attention for Image Super-ResolutionCode1
Motion-aware Contrastive Video Representation Learning via Foreground-background MergingCode1
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot FillingCode1
From t-SNE to UMAP with contrastive learningCode1
Efficient Zero-shot Event Extraction with Context-Definition AlignmentCode1
Contrastive Prototypical Network with Wasserstein Confidence PenaltyCode1
Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid ViewsCode1
Contrastive Learning with Adversarial Perturbations for Conditional Text GenerationCode1
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive LearningCode1
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
Improving Gloss-free Sign Language Translation by Reducing Representation DensityCode1
Multi-Grained Multimodal Interaction Network for Entity LinkingCode1
Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI ClassificationCode1
Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based LossesCode1
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity RecognitionCode1
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment ContrastCode1
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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