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 351–400 of 6661 papers

TitleStatusHype
Prompting Vision-Language Model for Nuclei Instance Segmentation and ClassificationCode0
Deep Learning for Forensic Identification of SourceCode0
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion—0
Contrastive Learning Guided Latent Diffusion Model for Image-to-Image Translation—0
DINeMo: Learning Neural Mesh Models with no 3D Annotations—0
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation—0
Taxonomy Inference for Tabular Data Using Large Language Models—0
Large Language Models Meet Contrastive Learning: Zero-Shot Emotion Recognition Across LanguagesCode0
Scaling Vision Pre-Training to 4K ResolutionCode7
SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI—0
A-MESS: Anchor based Multimodal Embedding with Semantic Synchronization for Multimodal Intent Recognition—0
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image AnalysisCode2
SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data PretrainingCode2
OCCO: LVM-guided Infrared and Visible Image Fusion Framework based on Object-aware and Contextual COntrastive Learning—0
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated DataCode0
Instruct-CLIP: Improving Instruction-Guided Image Editing with Automated Data Refinement Using Contrastive LearningCode1
Linguistics-aware Masked Image Modeling for Self-supervised Scene Text RecognitionCode1
Structuring Scientific Innovation: A Framework for Modeling and Discovering Impactful Knowledge Combinations—0
Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning—0
Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning—0
On the Perception Bottleneck of VLMs for Chart UnderstandingCode0
Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions—0
What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images—0
Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative SamplingCode0
Enhancing Persona Consistency for LLMs' Role-Playing using Persona-Aware Contrastive Learning—0
NaFM: Pre-training a Foundation Model for Small-Molecule Natural ProductsCode0
Neural-Guided Equation DiscoveryCode0
Should we pre-train a decoder in contrastive learning for dense prediction tasks?—0
Generative Modeling of Class Probability for Multi-Modal Representation Learning—0
Semi-supervised Cervical Segmentation on Ultrasound by A Dual Framework for Neural NetworksCode0
A Statistical Theory of Contrastive Learning via Approximate Sufficient Statistics—0
Federated Cross-Domain Click-Through Rate Prediction With Large Language Model Augmentation—0
GAIR: Improving Multimodal Geo-Foundation Model with Geo-Aligned Implicit Representations—0
DocVideoQA: Towards Comprehensive Understanding of Document-Centric Videos through Question Answering—0
Diffusion-augmented Graph Contrastive Learning for Collaborative Filter—0
Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation—0
UniSync: A Unified Framework for Audio-Visual Synchronization—0
PromptHash: Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing RetrievalCode0
Unlocking the Capabilities of Vision-Language Models for Generalizable and Explainable Deepfake Detection—0
Continual Multimodal Contrastive Learning—0
TULIP: Towards Unified Language-Image Pretraining—0
Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image ClassificationCode0
EgoDTM: Towards 3D-Aware Egocentric Video-Language PretrainingCode0
Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERUCode0
Text-Derived Relational Graph-Enhanced Network for Skeleton-Based Action SegmentationCode0
DashCLIP: Leveraging multimodal models for generating semantic embeddings for DoorDash—0
EEG-CLIP : Learning EEG representations from natural language descriptionsCode1
Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning—0
Unlocking the Potential of Unlabeled Data in Semi-Supervised Domain GeneralizationCode1
DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies—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