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 2201–2225 of 6661 papers

TitleStatusHype
Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport—0
DecisionNCE: Embodied Multimodal Representations via Implicit Preference LearningCode2
Polos: Multimodal Metric Learning from Human Feedback for Image CaptioningCode1
A Language Model based Framework for New Concept Placement in OntologiesCode1
FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems—0
TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful SpaceCode2
VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image AnalysisCode3
LocalGCL: Local-aware Contrastive Learning for Graphs—0
Demonstrating and Reducing Shortcuts in Vision-Language Representation LearningCode0
Analyzing Regional Organization of the Human Hippocampus in 3D-PLI Using Contrastive Learning and Geometric Unfolding—0
Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge GraphsCode1
COMAE: COMprehensive Attribute Exploration for Zero-shot Hashing—0
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings—0
High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs—0
Deep Contrastive Graph Learning with Clustering-Oriented Guidance—0
Making Pre-trained Language Models Better Continual Few-Shot Relation ExtractorsCode0
IRConStyle: Image Restoration Framework Using Contrastive Learning and Style TransferCode0
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks—0
CLIPose: Category-Level Object Pose Estimation with Pre-trained Vision-Language Knowledge—0
Debiased Model-based Interactive Recommendation—0
FGBERT: Function-Driven Pre-trained Gene Language Model for Metagenomics—0
Explainable Contrastive and Cost-Sensitive Learning for Cervical Cancer ClassificationCode0
Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving—0
CI w/o TN: Context Injection without Task Name for Procedure Planning—0
Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language ModelsCode0
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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