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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 3551–3600 of 6661 papers

TitleStatusHype
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation—0
Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations—0
MMPKUBase: A Comprehensive and High-quality Chinese Multi-modal Knowledge Graph—0
Safe Semi-Supervised Contrastive Learning Using In-Distribution Data as Positive Examples—0
A Multi-Source Heterogeneous Knowledge Injected Prompt Learning Method for Legal Charge Prediction—0
A Two-Stage Progressive Pre-training using Multi-Modal Contrastive Masked Autoencoders—0
Contrastive Learning and Abstract Concepts: The Case of Natural Numbers—0
StyEmp: Stylizing Empathetic Response Generation via Multi-Grained Prefix Encoder and Personality Reinforcement—0
Feedback Reciprocal Graph Collaborative Filtering—0
ConDL: Detector-Free Dense Image Matching—0
A Debiased Nearest Neighbors Framework for Multi-Label Text Classification—0
Contrastive Learning for Image Complexity Representation—0
Contrastive Learning for Knowledge-Based Question Generation in Large Language Models—0
BGM2Pose: Active 3D Human Pose Estimation with Non-Stationary Sounds—0
Learning Actionable World Models for Industrial Process Control—0
Beyond Prompting: An Efficient Embedding Framework for Open-Domain Question Answering—0
FRET: Feature Redundancy Elimination for Test Time Adaptation—0
MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generation—0
Fractal Graph Contrastive Learning—0
Aligning Proteins and Language: A Foundation Model for Protein Retrieval—0
Controllable Expressive 3D Facial Animation via Diffusion in a Unified Multimodal Space—0
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation—0
2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion—0
2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings—0
3D-Augmented Contrastive Knowledge Distillation for Image-based Object Pose Estimation—0
3D-Aware Encoding for Style-based Neural Radiance Fields—0
3D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion—0
3D Graph Contrastive Learning for Molecular Property Prediction—0
3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information—0
3D Neural Scene Representations for Visuomotor Control—0
3D pride without 2D prejudice: Bias-controlled multi-level generative models for structure-based ligand design—0
3D Scene Graph Guided Vision-Language Pre-training—0
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs—0
3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation—0
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations—0
4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding—0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation—0
AAVAE: Augmentation-Augmented Variational Autoencoders—0
A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition—0
Abnormal Event Detection via Hypergraph Contrastive Learning—0
Abnormality-Driven Representation Learning for Radiology Imaging—0
About contrastive unsupervised representation learning for classification and its convergence—0
A brief review of contrastive learning applied to astrophysics—0
Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information—0
Abstains from Prediction: Towards Robust Relation Extraction in Real World—0
Accelerating Augmentation Invariance Pretraining—0
ACCEPT: Diagnostic Forecasting of Battery Degradation Through Contrastive Learning—0
ACCon: Angle-Compensated Contrastive Regularizer for Deep Regression—0
ACC-ViT : Atrous Convolution's Comeback in Vision Transformers—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