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 2401–2450 of 6661 papers

TitleStatusHype
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization—0
Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech—0
TransliCo: A Contrastive Learning Framework to Address the Script Barrier in Multilingual Pretrained Language ModelsCode0
CCFC: Bridging Federated Clustering and Contrastive LearningCode0
Secrets of RLHF in Large Language Models Part II: Reward ModelingCode5
Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI FeedbackCode1
HiCMAE: Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion RecognitionCode2
Learn From Zoom: Decoupled Supervised Contrastive Learning For WCE Image ClassificationCode2
Enhancing Contrastive Learning with Efficient Combinatorial Positive Pairing—0
End-to-end Learnable Clustering for Intent Learning in RecommendationCode2
ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration—0
Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recordingCode1
Relaxed Contrastive Learning for Federated LearningCode1
SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image—0
BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguation—0
ECC-PolypDet: Enhanced CenterNet with Contrastive Learning for Automatic Polyp DetectionCode0
Improving the Robustness of Knowledge-Grounded Dialogue via Contrastive LearningCode0
Connect Later: Improving Fine-tuning for Robustness with Targeted AugmentationsCode0
Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking—0
Two-stream joint matching method based on contrastive learning for few-shot action recognition—0
Efficient Selective Audio Masked Multimodal Bottleneck Transformer for Audio-Video Classification—0
End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction—0
Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent PredictionCode0
Unifying Graph Contrastive Learning via Graph Message Augmentation—0
Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech RepresentationCode0
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated LearningCode2
QoS-Aware Graph Contrastive Learning for Web Service Recommendation—0
Preserving Silent Features for Domain Generalization—0
Exploiting Data Hierarchy as a New Modality for Contrastive Learning—0
Enhancing Context Through Contrast—0
Unsupervised hard Negative Augmentation for contrastive learningCode0
Multi-Stage Contrastive Regression for Action Quality AssessmentCode0
Graph-level Protein Representation Learning by Structure Knowledge Refinement—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—0
Learning Multimodal Volumetric Features for Large-Scale Neuron TracingCode0
Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-IdentificationCode1
Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation—0
Task Oriented Dialogue as a Catalyst for Self-Supervised Automatic Speech RecognitionCode0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
Multi-modal vision-language model for generalizable annotation-free pathology localization and clinical diagnosisCode1
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning—0
SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks—0
Multimodal self-supervised learning for lesion localization—0
MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning—0
ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual ClassificationCode0
AliFuse: Aligning and Fusing Multi-modal Medical Data for Computer-Aided DiagnosisCode0
Unsupervised Continual Anomaly Detection with Contrastively-learned PromptCode2
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive LearningCode1
Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces—0
A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection—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