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Methodology
← Learning Types
Machine Learning
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Learning Types
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Contrastive Learning
3535 directly classified papers
Papers per year
2011: 1
2013: 1
2014: 1
2017: 3
2018: 10
2019: 26
2020: 88
2021: 394
2022: 627
2023: 852
2024: 698
2025: 616
2026: 218
Papers
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors
NIPS 2023
Are These the Same Apple? Comparing Images Based on Object Intrinsics
NIPS 2023
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
NIPS 2023
Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation
NIPS 2023
Learning Fine-grained View-Invariant Representations from Unpaired Ego-Exo Videos via Temporal Alignment
NIPS 2023
Keypoint-Augmented Self-Supervised Learning for Medical Image Segmentation with Limited Annotation
NIPS 2023
TopoSRL: Topology preserving self-supervised Simplicial Representation Learning
NIPS 2023
CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss
NIPS 2023
CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders
NIPS 2023
Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion
NIPS 2023
Complementary Benefits of Contrastive Learning and Self-Training Under Distribution Shift
NIPS 2023
Connecting Multi-modal Contrastive Representations
NIPS 2023
Identifiable Contrastive Learning with Automatic Feature Importance Discovery
NIPS 2023
Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation
NIPS 2023
Modality-Independent Teachers Meet Weakly-Supervised Audio-Visual Event Parser
NIPS 2023
InfoCD: A Contrastive Chamfer Distance Loss for Point Cloud Completion
NIPS 2023
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection
NIPS 2023
MIM4DD: Mutual Information Maximization for Dataset Distillation
NIPS 2023
Towards robust and generalizable representations of extracellular data using contrastive learning
NIPS 2023
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning
NIPS 2023
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
NIPS 2023
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion
NIPS 2023
Weakly-Supervised Audio-Visual Segmentation
NIPS 2023
FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations
NIPS 2023
Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
NIPS 2023
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