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 576–600 of 6661 papers

TitleStatusHype
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation—0
T-SCEND: Test-time Scalable MCTS-enhanced Diffusion ModelCode1
Efficient Domain Adaptation of Multimodal Embeddings using Constrastive Learning—0
Mask-informed Deep Contrastive Incomplete Multi-view ClusteringCode0
Multi-level Supervised Contrastive Learning—0
VisTA: Vision-Text Alignment Model with Contrastive Learning using Multimodal Data for Evidence-Driven, Reliable, and Explainable Alzheimer's Disease Diagnosis—0
Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied AgentsCode0
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis—0
BC-GAN: A Generative Adversarial Network for Synthesizing a Batch of Collocated Clothing—0
General Feature Extraction In SAR Target Classification: A Contrastive Learning Approach Across Sensor TypesCode0
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning—0
neuro2voc: Decoding Vocalizations from Neural ActivityCode0
CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive LearningCode1
Contrastive Forward-Forward: A Training Algorithm of Vision Transformer—0
Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant CancerCode1
Improving vision-language alignment with graph spiking hybrid Networks—0
DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition—0
Improving Multi-Label Contrastive Learning by Leveraging Label Distribution—0
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction NetworksCode0
A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series—0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
Sebra: Debiasing Through Self-Guided Bias RankingCode0
IROAM: Improving Roadside Monocular 3D Object Detection Learning from Autonomous Vehicle Data Domain—0
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features—0
Deconstruct Complexity (DeComplex): A Novel Perspective on Tackling Dense Action 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