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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 2151–2175 of 6661 papers

TitleStatusHype
RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability—0
ContrastiveGaussian: High-Fidelity 3D Generation with Contrastive Learning and Gaussian SplattingCode0
Benchmarking Image Embeddings for E-Commerce: Evaluating Off-the Shelf Foundation Models, Fine-Tuning Strategies and Practical Trade-offs—0
CHIME: A Compressive Framework for Holistic Interest Modeling—0
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection—0
Unifying Search and Recommendation: A Generative Paradigm Inspired by Information Theory—0
Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation—0
Large Language Models Enhanced Hyperbolic Space Recommender Systems—0
TC-MGC: Text-Conditioned Multi-Grained Contrastive Learning for Text-Video RetrievalCode0
MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image SegmentationCode0
Sub-Clustering for Class Distance Recalculation in Long-Tailed Drug Classification—0
Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative FilteringCode0
QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors—0
ADA-Net: Attention-Guided Domain Adaptation Network with Contrastive Learning for Standing Dead Tree Segmentation Using Aerial ImageryCode0
ATM-Net: Anatomy-Aware Text-Guided Multi-Modal Fusion for Fine-Grained Lumbar Spine Segmentation—0
Decentralized Collective World Model for Emergent Communication and Coordination—0
AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video HashingCode0
Comparative Analysis of Unsupervised and Supervised Autoencoders for Nuclei Classification in Clear Cell Renal Cell Carcinoma Images—0
FontGuard: A Robust Font Watermarking Approach Leveraging Deep Font KnowledgeCode0
SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions—0
SLACK: Attacking LiDAR-based SLAM with Adversarial Point Injections—0
Group-based Distinctive Image Captioning with Memory Difference Encoding and Attention—0
All-day Depth Completion via Thermal-LiDAR Fusion—0
Direction-Aware Hybrid Representation Learning for 3D Hand Pose and Shape Estimation—0
Overlap-Aware Feature Learning for Robust Unsupervised Domain Adaptation for 3D Semantic Segmentation—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