Today I am talking to Philip Winter, researcher at the Medical Imaging group of the VRVis, a research center for virtual realities and visualizations.

Philip will explain the benefits and challenges in continual learning and will present his recent paper "PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks". Where he and his colleagues have developed a system that uses a frozen hierarchical feature extractor to build a memory database out of the labeled training data. During inference the system identified training examples similar to the test data and prediction is performed through a combination of parameter-free correspondence matching and message passing based on the closes training datapoints.

I hope you enjoy this episode and will find it useful.

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## TOC

00:00:00 Beginning

00:03:04 Guest Introduction

00:06:50 What is continual learning?

00:15:38 Catastrophic forgetting

00:27:36 Paper: Parmesan

00:40:14 Composing Ensembles

00:46:12 How to build memory over time

00:55:37 Limitations of Parmesan

### References

Philip Winter - https://www.linkedin.com/in/philip-m-winter-msc-b15679129/

VRVIS - https://www.vrvis.at/

PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks - https://arxiv.org/abs/2403.11743

Continual Learning Survey: https://arxiv.org/pdf/1909.08383