# One-shot learning for the long term: consolidation with an artificial hippocampal algorithm

arXiv: https://arxiv.org/abs/2102.07503 · HTML: https://mlsanity.com/paper/2102.07503/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [k-Sparse Autoencoders](https://mlsanity.com/paper/1312.5663/) (2013)
- [Low-Shot Learning from Imaginary Data](https://mlsanity.com/paper/1801.05401/) (2018)
- [A continual learning survey: Defying forgetting in classification tasks](https://mlsanity.com/paper/1909.08383/) (2019)
- [Continuous Learning in Single-Incremental-Task Scenarios](https://mlsanity.com/paper/1806.08568/) (2018)
- [The Omniglot challenge: a 3-year progress report](https://mlsanity.com/paper/1902.03477/) (2019)
- [Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization](https://mlsanity.com/paper/1805.10966/) (2018)
- [Defining Benchmarks for Continual Few-Shot Learning](https://mlsanity.com/paper/2004.11967/) (2020)
- [AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning](https://mlsanity.com/paper/1909.10340/) (2019)
- [Unsupervised One-shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal Architecture](https://mlsanity.com/paper/2010.15999/) (2020)

## Built on by (most cited first, 1 of 1)

- [Expanding continual few-shot learning benchmarks to include recognition of specific instances](https://mlsanity.com/paper/2209.07863/) (2022)

## Abstract (arXiv)

Standard few-shot experiments involve learning to efficiently match previously unseen samples by class. We claim that few-shot learning should be long term, assimilating knowledge for the future, without forgetting previous concepts. In the mammalian brain, the hippocampus is understood to play a significant role in this process, by learning rapidly and consolidating knowledge to the neocortex incrementally over a short period. In this research we tested whether an artificial hippocampal algorithm (AHA), could be used with a conventional Machine Learning (ML) model that learns incrementally analogous to the neocortex, to achieve one-shot learning both short and long term. The results demonstrated that with the addition of AHA, the system could learn in one-shot and consolidate the knowledge for the long term without catastrophic forgetting. This study is one of the first examples of using a CLS model of hippocampus to consolidate memories, and it constitutes a step toward few-shot continual learning.