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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
Energy-Based Contrastive Learning of Visual Representations
NIPS 2022
CyCLIP: Cyclic Contrastive Language-Image Pretraining
NIPS 2022
Evaluating Graph Generative Models with Contrastively Learned Features
NIPS 2022
SelecMix: Debiased Learning by Contradicting-pair Sampling
NIPS 2022
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning
NIPS 2022
Tailoring Visual Object Representations to Human Requirements: A Case Study with a Recycling Robot
CORL 2022
Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions
NIPS 2022
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
NIPS 2022
Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?
NIPS 2022
Unsupervised Reinforcement Learning with Contrastive Intrinsic Control
NIPS 2022
Contrastive Learning as Goal-Conditioned Reinforcement Learning
NIPS 2022
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training
NIPS 2022
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency
NIPS 2022
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning
NIPS 2022
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts
NIPS 2022
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
NIPS 2022
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
NIPS 2022
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
NIPS 2022
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
NIPS 2022
Label-invariant Augmentation for Semi-Supervised Graph Classification
NIPS 2022
Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective
NIPS 2022
Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
NIPS 2022
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification
NIPS 2022
A Contrastive Framework for Neural Text Generation
NIPS 2022
PALI at SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts
SEMEVAL 2022
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