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 2251–2300 of 6661 papers

TitleStatusHype
Learning the Unlearned: Mitigating Feature Suppression in Contrastive LearningCode1
HU at SemEval-2024 Task 8A: Can Contrastive Learning Learn Embeddings to Detect Machine-Generated Text?Code0
PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay ReconstructionCode0
A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation—0
Continuous Multi-Task Pre-training for Malicious URL Detection and Webpage ClassificationCode1
Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative FilteringCode2
Assessing News Thumbnail Representativeness: Counterfactual text can enhance the cross-modal matching abilityCode0
Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning—0
Parametric Augmentation for Time Series Contrastive LearningCode1
A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models—0
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation—0
Training Class-Imbalanced Diffusion Model Via Overlap OptimizationCode0
Pretext Training Algorithms for Event Sequence Data—0
Dynamic Patch-aware Enrichment Transformer for Occluded Person Re-Identification—0
Multiview Contrastive Learning for Unsupervised Domain Adaptation in Brain–Computer Interfaces—0
MM-Point: Multi-View Information-Enhanced Multi-Modal Self-Supervised 3D Point Cloud UnderstandingCode1
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained RepresentationsCode1
f-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning—0
Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention—0
Low-Rank Graph Contrastive Learning for Node Classification—0
Integrating ChatGPT into Secure Hospital Networks: A Case Study on Improving Radiology Report Analysis—0
Disambiguated Node Classification with Graph Neural NetworksCode0
AMEND: A Mixture of Experts Framework for Long-tailed Trajectory Prediction—0
DNABERT-S: Pioneering Species Differentiation with Species-Aware DNA EmbeddingsCode2
Modeling Balanced Explicit and Implicit Relations with Contrastive Learning for Knowledge Concept Recommendation in MOOCs—0
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection—0
FESS Loss: Feature-Enhanced Spatial Segmentation Loss for Optimizing Medical Image AnalysisCode0
Contrastive Learning for Regression on Hyperspectral Data—0
Injecting Wiktionary to improve token-level contextual representations using contrastive learning—0
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive LearningCode2
Topic Modeling as Multi-Objective Contrastive Optimization—0
SemTra: A Semantic Skill Translator for Cross-Domain Zero-Shot Policy Adaptation—0
Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image SegmentationCode4
Generalizing Conversational Dense Retrieval via LLM-Cognition Data AugmentationCode0
Rethinking Graph Masked Autoencoders through Alignment and UniformityCode0
CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion RecognitionCode0
Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning—0
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive LossCode1
Learning Contrastive Feature Representations for Facial Action Unit DetectionCode0
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain—0
Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning—0
Using YOLO v7 to Detect Kidney in Magnetic Resonance Imaging—0
CounterCLR: Counterfactual Contrastive Learning with Non-random Missing Data in Recommendation—0
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs—0
Large Language Model Meets Graph Neural Network in Knowledge Distillation—0
Adaptive Hypergraph Network for Trust PredictionCode0
Multi-Patch Prediction: Adapting LLMs for Time Series Representation LearningCode2
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance SegmentationCode1
Improved Generalization of Weight Space Networks via AugmentationsCode0
CAMBranch: Contrastive Learning with Augmented MILPs for Branching—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