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 601–650 of 6661 papers

TitleStatusHype
Beyond Co-occurrence: Multi-modal Session-based RecommendationCode1
Contrastive learning for regression in multi-site brain age predictionCode1
Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class DiscoveryCode1
Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth Image PairsCode1
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure LearningCode1
Contrastive Learning for Compact Single Image DehazingCode1
Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language ModelsCode1
Contrastive Learning Inverts the Data Generating ProcessCode1
Contrastive Learning for Improving ASR Robustness in Spoken Language UnderstandingCode1
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NERCode1
AD-CLIP: Adapting Domains in Prompt Space Using CLIPCode1
Contrastive learning of global and local features for medical image segmentation with limited annotationsCode1
Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging DataCode1
Big Self-Supervised Models Advance Medical Image ClassificationCode1
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple LanguagesCode1
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative AdversariesCode1
Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data AugmentationsCode1
Contrastive Learning of User Behavior Sequence for Context-Aware Document RankingCode1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
From t-SNE to UMAP with contrastive learningCode1
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight DetectionCode1
Contrastive Learning and Mixture of Experts Enables Precise Vector EmbeddingsCode1
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
Biomedical Entity Linking with Contrastive Context MatchingCode1
Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy DetectionCode1
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at ScaleCode1
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and MappingCode1
Anatomical Foundation Models for Brain MRIsCode1
Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image AnalysisCode1
C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender SystemCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Black-Box Attack against GAN-Generated Image Detector with Contrastive PerturbationCode1
A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving SystemsCode1
Black Box Few-Shot Adaptation for Vision-Language modelsCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
Blind Localization and Clustering of Anomalies in TexturesCode1
Contrastive Learning for Knowledge TracingCode1
CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive LearningCode1
Contrastive Representation Learning for Exemplar-Guided Paraphrase GenerationCode1
Contrastive Representation Learning for Dynamic Link Prediction in Temporal NetworksCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
Boosting Contrastive Self-Supervised Learning with False Negative CancellationCode1
Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical StructuresCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
Contrastive Fine-grained Class Clustering via Generative Adversarial NetworksCode1
Contrastive Trajectory Similarity Learning with Dual-Feature AttentionCode1
AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR DataCode1
Benchmarking Omni-Vision Representation through the Lens of Visual RealmsCode1
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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