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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 301–325 of 6661 papers

TitleStatusHype
CMIP-CIL: A Cross-Modal Benchmark for Image-Point Class Incremental LearningCode0
ContrastiveGaussian: High-Fidelity 3D Generation with Contrastive Learning and Gaussian SplattingCode0
Impact of Language Guidance: A Reproducibility Study—0
GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video SegmentationCode2
Leveraging LLMs for Multimodal Retrieval-Augmented Radiology Report Generation via Key Phrase Extraction—0
RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability—0
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
Unifying Search and Recommendation: A Generative Paradigm Inspired by Information Theory—0
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection—0
Generalized Semantic Contrastive Learning via Embedding Side Information for Few-Shot Object DetectionCode2
GOLLuM: Gaussian Process Optimized LLMs -- Reframing LLM Finetuning through Bayesian OptimizationCode1
Large Language Models Enhanced Hyperbolic Space Recommender Systems—0
Retrieval Augmented Generation with Collaborative Filtering for Personalized Text GenerationCode1
Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation—0
MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image SegmentationCode0
TC-MGC: Text-Conditioned Multi-Grained Contrastive Learning for Text-Video RetrievalCode0
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
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
Decentralized Collective World Model for Emergent Communication and Coordination—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