# Few-Shot Learning with Metric-Agnostic Conditional Embeddings

arXiv: https://arxiv.org/abs/1802.04376 · HTML: https://mlsanity.com/paper/1802.04376/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2018.

## Built on by (most cited first, 30 of 40)

- [Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning](https://mlsanity.com/paper/2102.03983/) (2021)
- [Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition](https://mlsanity.com/paper/2101.02833/) (2021)
- [Multi-Objective Meta Learning](https://mlsanity.com/paper/2102.07121/) (2021)
- [Do Vision-Language Pretrained Models Learn Composable Primitive Concepts?](https://mlsanity.com/paper/2203.17271/) (2022)
- [Meta-learning approaches for few-shot learning: A survey of recent advances](https://mlsanity.com/paper/2303.07502/) (2023)
- [FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations](https://mlsanity.com/paper/2203.16639/) (2022)
- [Reinforced Attention for Few-Shot Learning and Beyond](https://mlsanity.com/paper/2104.04192/) (2021)
- [Disentangled Feature Representation for Few-shot Image Classification](https://mlsanity.com/paper/2109.12548/) (2021)
- [Towards Task Sampler Learning for Meta-Learning](https://mlsanity.com/paper/2307.08924/) (2023)
- [DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor Neural Network](https://mlsanity.com/paper/2103.08160/) (2021)
- [CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification](https://mlsanity.com/paper/2203.13465/) (2022)
- [FS-BAN: Born-Again Networks for Domain Generalization Few-Shot Classification](https://mlsanity.com/paper/2208.10930/) (2022)
- [Meta-Learning with Self-Improving Momentum Target](https://mlsanity.com/paper/2210.05185/) (2022)
- [Few-shot Metric Learning: Online Adaptation of Embedding for Retrieval](https://mlsanity.com/paper/2211.07116/) (2022)
- [On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation](https://mlsanity.com/paper/2206.07260/) (2022)
- [Tensor feature hallucination for few-shot learning](https://mlsanity.com/paper/2106.05321/) (2021)
- [Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation](https://mlsanity.com/paper/2109.02915/) (2021)
- [Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning](https://mlsanity.com/paper/2110.07510/) (2021)
- [On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification](https://mlsanity.com/paper/2205.00895/) (2022)
- [Few-Shot Learning of Compact Models via Task-Specific Meta Distillation](https://mlsanity.com/paper/2210.09922/) (2022)
- [Local Propagation for Few-Shot Learning](https://mlsanity.com/paper/2101.01480/) (2021)
- [Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition](https://mlsanity.com/paper/2109.01302/) (2021)
- [Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks](https://mlsanity.com/paper/2205.15619/) (2022)
- [Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data](https://mlsanity.com/paper/2107.10387/) (2021)
- [A Simple Approach to Adversarial Robustness in Few-shot Image Classification](https://mlsanity.com/paper/2204.05432/) (2022)
- [Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning](https://mlsanity.com/paper/2404.04434/) (2024)
- [Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains](https://mlsanity.com/paper/2112.13539/) (2021)
- [Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning](https://mlsanity.com/paper/2308.02173/) (2023)
- [Few-shot learning via tensor hallucination](https://mlsanity.com/paper/2104.09467/) (2021)
- [GCCN: Global Context Convolutional Network](https://mlsanity.com/paper/2110.11664/) (2021)

## 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.