mlsanityData to 7 Oct 2026

Few-Shot Learning with Metric-Agnostic Conditional Embeddings

arXiv 1802.04376 ↗First posted 12 Feb 2018cs.LG stat.MLcited by 40

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Abstract (arXiv)

Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning. To accomplish this, we introduce a novel architecture where class representations are conditioned for each few-shot trial based on a target image. We also deviate from traditional metric-learning approaches by training a network to perform comparisons between classes rather than relying on a static metric comparison. This allows the network to decide what aspects of each class are important for the comparison at hand. We find that this flexible architecture works well in practice, achieving state-of-the-art performance on the Caltech-UCSD birds fine-grained classification task.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Feb 2018.

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Citing papers per year: 2021: 16; 2022: 14; 2023: 6; 2024: 2; 2025: 2.

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  1. 2021Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learningcited by 72
  2. 2021Shallow Bayesian Meta Learning for Real-World Few-Shot Recognitioncited by 48
  3. 2021Multi-Objective Meta Learningcited by 39
  4. 2022Do Vision-Language Pretrained Models Learn Composable Primitive Concepts?cited by 31
  5. 2023Meta-learning approaches for few-shot learning: A survey of recent advancescited by 25
  6. 2022FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relationscited by 20
  7. 2021Reinforced Attention for Few-Shot Learning and Beyondcited by 18
  8. 2021Disentangled Feature Representation for Few-shot Image Classificationcited by 12
  9. 2023Towards Task Sampler Learning for Meta-Learningcited by 11
  10. 2021DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor Neural Networkcited by 9
  11. 2022CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classificationcited by 9
  12. 2022FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classificationcited by 9
28 more
  1. 2022Meta-Learning with Self-Improving Momentum Targetcited by 9
  2. 2022Few-shot Metric Learning: Online Adaptation of Embedding for Retrievalcited by 8
  3. 2022On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptationcited by 7
  4. 2021Tensor feature hallucination for few-shot learningcited by 6
  5. 2021Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formationcited by 5
  6. 2021Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learningcited by 5
  7. 2022On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classificationcited by 5
  8. 2022Few-Shot Learning of Compact Models via Task-Specific Meta Distillationcited by 5
  9. 2021Local Propagation for Few-Shot Learningcited by 4
  10. 2021Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decompositioncited by 4
  11. 2022Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networkscited by 4
  12. 2021Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Datacited by 3
  13. 2022A Simple Approach to Adversarial Robustness in Few-shot Image Classificationcited by 3
  14. 2024Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruningcited by 3
  15. 2021Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domainscited by 2
  16. 2023Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learningcited by 2
  17. 2021Few-shot learning via tensor hallucinationcited by 1
  18. 2021GCCN: Global Context Convolutional Networkcited by 1
  19. 2022A Unified Framework with Meta-dropout for Few-shot Learningcited by 1
  20. 2023Exploring Active Learning in Meta-Learning: Enhancing Context Set Labelingcited by 1
  21. 2024Improved Few-Shot Image Classification Through Multiple-Choice Questionscited by 1
  22. 2025Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Expertscited by 1
  23. 2021Will Multi-modal Data Improves Few-shot Learning?cited by 0
  24. 2022A Framework of Meta Functional Learning for Regularising Knowledge Transfercited by 0
  25. 2022Fine-grained Few-shot Recognition by Deep Object Parsingcited by 0
  26. 2023Explore the Power of Dropout on Few-shot Learningcited by 0
  27. 2023When the Few Outweigh the Many: Illicit Content Recognition with Few-Shot Learningcited by 0
  28. 2025Semantic-Anchored, Class Variance-Optimized Clustering for Robust Semi-Supervised Few-Shot Learningcited by 0