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 1601–1650 of 6661 papers

TitleStatusHype
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection—0
Semantic Compositions Enhance Vision-Language Contrastive Learning—0
Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution AnalysisCode2
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations—0
SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTsCode0
LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement—0
InfoNCE: Identifying the Gap Between Theory and Practice—0
eMoE-Tracker: Environmental MoE-based Transformer for Robust Event-guided Object Tracking—0
ProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation—0
Rethinking and Defending Protective Perturbation in Personalized Diffusion ModelsCode1
Local Manifold Learning for No-Reference Image Quality Assessment—0
Zero-shot domain adaptation based on dual-level mix and contrast—0
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers—0
Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics—0
Denoising as Adaptation: Noise-Space Domain Adaptation for Image RestorationCode2
Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive LearningCode0
Selective Prompting Tuning for Personalized Conversations with LLMsCode1
Retrieval-style In-Context Learning for Few-shot Hierarchical Text ClassificationCode1
Data curation via joint example selection further accelerates multimodal learning—0
TopoGCL: Topological Graph Contrastive LearningCode0
Investigating Self-Supervised Methods for Label-Efficient Learning—0
MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation LearningCode0
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System—0
Contrastive General Graph Matching with Adaptive Augmentation Sampling—0
Video Inpainting Localization with Contrastive LearningCode1
Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-MakingCode1
The Championship-Winning Solution for the 5th CLVISION Challenge 2024—0
Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments—0
Enhancing OOD Detection Using Latent DiffusionCode0
MuseCL: Predicting Urban Socioeconomic Indicators via Multi-Semantic Contrastive LearningCode1
Speech Analysis of Language Varieties in ItalyCode0
Fine-grained Background Representation for Weakly Supervised Semantic SegmentationCode0
Self-Supervised Alignment Learning for Medical Image Segmentation—0
DN-CL: Deep Symbolic Regression against Noise via Contrastive Learning—0
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss—0
TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
A Contrastive Learning Approach to Mitigate Bias in Speech ModelsCode0
LARP: Language Audio Relational Pre-training for Cold-Start Playlist ContinuationCode0
Factual Dialogue Summarization via Learning from Large Language Models—0
Unifying Graph Convolution and Contrastive Learning in Collaborative FilteringCode1
Maintenance Required: Updating and Extending Bootstrapped Human Activity Recognition Systems for Smart Homes—0
Revealing Vision-Language Integration in the Brain with Multimodal NetworksCode0
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning PerspectiveCode1
Towards a multimodal framework for remote sensing image change retrieval and captioningCode0
Composite Concept Extraction through Backdooring—0
Visually Robust Adversarial Imitation Learning from Videos with Contrastive LearningCode0
Rethinking Knee Osteoarthritis Severity Grading: A Few Shot Self-Supervised Contrastive Learning Approach—0
A Generic Method for Fine-grained Category Discovery in Natural Language Texts—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