mlsanityData to 14 Oct 2026

Adversarial Contrastive Estimation

arXiv 1805.03642 ↗First posted 9 May 2018cs.CL cs.AI cs.LGcited by 23

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Abstract (arXiv)

Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach. In this work, we view contrastive learning as an abstraction of all such methods and augment the negative sampler into a mixture distribution containing an adversarially learned sampler. The resulting adaptive sampler finds harder negative examples, which forces the main model to learn a better representation of the data. We evaluate our proposal on learning word embeddings, order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 May 2018.

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Citing papers per year: 2020: 1; 2021: 9; 2022: 3; 2023: 6; 2024: 3; 2025: 1.

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  1. 2021How to Train Your Energy-Based Modelscited by 274
  2. 2024Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM's Reasoning Capabilitycited by 83
  3. 2021Negative Data Augmentationcited by 46
  4. 2020Energy-Based Imitation Learningcited by 40
  5. 2021Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problemscited by 30
  6. 2024Does Negative Sampling Matter? A Review with Insights into its Theory and Applicationscited by 15
  7. 2021Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularizationcited by 14
  8. 2021Contrastive Attraction and Contrastive Repulsion for Representation Learningcited by 13
  9. 2021Towards User-Driven Neural Machine Translationcited by 10
  10. 2022Self-supervised Context-aware Style Representation for Expressive Speech Synthesiscited by 10
  11. 2023Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Modelscited by 10
  12. 2021Adversarial Contrastive Pre-training for Protein Sequencescited by 8
11 more
  1. 2021Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effectscited by 7
  2. 2023Moment Matching Denoising Gibbs Samplingcited by 7
  3. 2022Gradient-Guided Importance Sampling for Learning Binary Energy-Based Modelscited by 6
  4. 2023Learning Unnormalized Statistical Models via Compositional Optimizationcited by 6
  5. 2023Data-Driven Stochastic Motion Evaluation and Optimization with Image by Spatially-Aligned Temporal Encodingcited by 3
  6. 2023ContrastNER: Contrastive-based Prompt Tuning for Few-shot NERcited by 3
  7. 2025C-LEAD: Contrastive Learning for Enhanced Adversarial Defensecited by 2
  8. 2021TFDPM: Attack detection for cyber-physical systems with diffusion probabilistic modelscited by 1
  9. 2022Self-Adapting Noise-Contrastive Estimation for Energy-Based Modelscited by 1
  10. 2023Adversarially Contrastive Estimation of Conditional Neural Processescited by 1
  11. 2024FluentEditor2: Text-based Speech Editing by Modeling Multi-Scale Acoustic and Prosody Consistencycited by 1