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 1001–1025 of 6661 papers

TitleStatusHype
Learning General-Purpose Biomedical Volume Representations using Randomized SynthesisCode2
FPPL: An Efficient and Non-IID Robust Federated Continual Learning FrameworkCode0
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud RegistrationCode0
Co-clustering for Federated Recommender SystemCode1
DPCL-Diff: The Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning—0
Learning Hidden Subgoals under Temporal Ordering Constraints in Reinforcement Learning—0
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination—0
OSAD: Open-Set Aircraft Detection in SAR Images—0
Negative-Free Self-Supervised Gaussian Embedding of GraphsCode0
Multi-Channel Hypergraph Contrastive Learning for Matrix Completion—0
LEARNER: Learning Granular Labels from Coarse Labels using Contrastive Learning—0
FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration DetectionCode0
Replace-then-Perturb: Targeted Adversarial Attacks With Visual Reasoning for Vision-Language Models—0
Tumor Location-weighted MRI-Report Contrastive Learning: A Framework for Improving the Explainability of Pediatric Brain Tumor Diagnosis—0
Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited ModalitiesCode1
How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method—0
Metric Learning for 3D Point Clouds Using Optimal Transport—0
Language-Assisted Skeleton Action Understanding for Skeleton-Based Temporal Action SegmentationCode1
Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images—0
Topology-Aware Graph Augmentation for Predicting Clinical Trajectories in Neurocognitive Disorders—0
Towards Cross-Modal Text-Molecule Retrieval with Better Modality AlignmentCode0
JEMA: A Joint Embedding Framework for Scalable Co-Learning with Multimodal Alignment—0
BioNCERE: Non-Contrastive Enhancement For Relation Extraction In Biomedical Texts—0
EchoFM: Foundation Model for Generalizable Echocardiogram AnalysisCode1
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation—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