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 3001–3050 of 6661 papers

TitleStatusHype
Exploring Semantic Consistency in Unpaired Image Translation to Generate Data for Surgical ApplicationsCode0
Diffusion Model is Secretly a Training-free Open Vocabulary Semantic SegmenterCode1
ATM: Action Temporality Modeling for Video Question Answering—0
SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extractionCode1
Graph Self-Contrast Representation Learning—0
AVATAR: Robust Voice Search Engine Leveraging Autoregressive Document Retrieval and Contrastive Learning—0
Memory augment is All You Need for image restorationCode1
Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition—0
Multi-Relational Contrastive Learning for RecommendationCode1
M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning—0
Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive LearningCode1
DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate AppraisalCode0
Contrastive Grouping with Transformer for Referring Image SegmentationCode1
Contrastive Feature Masking Open-Vocabulary Vision Transformer—0
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative samplingCode0
Towards Contrastive Learning in Music Video Domain—0
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation LearningCode0
Learning Speech Representation From Contrastive Token-Acoustic Pretraining—0
Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts—0
MoMA: Momentum Contrastive Learning with Multi-head Attention-based Knowledge Distillation for Histopathology Image AnalysisCode0
Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images—0
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs—0
Supervised Contrastive Learning with Nearest Neighbor Search for Speech Emotion Recognition—0
Robust Representation Learning for Unreliable Partial Label Learning—0
IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation—0
Towards a Rigorous Analysis of Mutual Information in Contrastive Learning—0
Multimodal Contrastive Learning and Tabular Attention for Automated Alzheimer's Disease Prediction—0
Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation—0
When hard negative sampling meets supervised contrastive learning—0
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NERCode1
Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-DistillationCode1
LAC: Latent Action Composition for Skeleton-based Action Segmentation—0
Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?Code0
Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance—0
Multi-Scale and Multi-Layer Contrastive Learning for Domain GeneralizationCode0
Hierarchical Contrastive Learning for Pattern-Generalizable Image Corruption DetectionCode1
Forensic Histopathological Recognition via a Context-Aware MIL Network Powered by Self-Supervised Contrastive LearningCode0
Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation—0
Towards Fast and Accurate Image-Text Retrieval with Self-Supervised Fine-Grained AlignmentCode1
PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism DiagnosisCode0
Decoding Natural Images from EEG for Object RecognitionCode1
Self-Supervised Representation Learning with Cross-Context Learning between Global and Hypercolumn Features—0
A Small and Fast BERT for Chinese Medical Punctuation RestorationCode0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records—0
A Co-training Approach for Noisy Time Series Learning—0
Cross-Video Contextual Knowledge Exploration and Exploitation for Ambiguity Reduction in Weakly Supervised Temporal Action Localization—0
FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious disease—0
Age Prediction From Face Images Via Contrastive Learning—0
Understanding Dark Scenes by Contrasting Multi-Modal ObservationsCode1
Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors—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