# Discriminative k-shot learning using probabilistic models

arXiv: https://arxiv.org/abs/1706.00326 · HTML: https://mlsanity.com/paper/1706.00326/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2017.

## Built on by (most cited first, 12 of 12)

- [HyperShot: Few-Shot Learning by Kernel HyperNetworks](https://mlsanity.com/paper/2203.11378/) (2022)
- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks](https://mlsanity.com/paper/2205.15745/) (2022)
- [Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification](https://mlsanity.com/paper/2206.09843/) (2022)
- [Blind Users Accessing Their Training Images in Teachable Object Recognizers](https://mlsanity.com/paper/2208.07968/) (2022)
- [Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation](https://mlsanity.com/paper/2306.13554/) (2023)
- [How to Do Machine Learning with Small Data? -- A Review from an Industrial Perspective](https://mlsanity.com/paper/2311.07126/) (2023)
- [MetaKernel: Learning Variational Random Features with Limited Labels](https://mlsanity.com/paper/2105.03781/) (2021)
- [Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning](https://mlsanity.com/paper/2110.13188/) (2021)
- [A Transductive Maximum Margin Classifier for Few-Shot Learning](https://mlsanity.com/paper/2107.11975/) (2021)
- [Learning by Examples Based on Multi-level Optimization](https://mlsanity.com/paper/2109.10824/) (2021)
- [Hypernetwork approach to Bayesian MAML](https://mlsanity.com/paper/2210.02796/) (2022)

## Abstract (arXiv)

This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task comprising new classes and small numbers of examples. The new approach not only leverages the feature-based representation learned by a neural network from the initial task (representational transfer), but also information about the classes (concept transfer). The concept information is encapsulated in a probabilistic model for the final layer weights of the neural network which acts as a prior for probabilistic k-shot learning. We show that even a simple probabilistic model achieves state-of-the-art on a standard k-shot learning dataset by a large margin. Moreover, it is able to accurately model uncertainty, leading to well calibrated classifiers, and is easily extensible and flexible, unlike many recent approaches to k-shot learning.