59. Philip Winter - VRVis - Continual Learning
from Austrian Artificial Intelligence Podcast Hosted by Manuel Pasieka
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. ## AAIP Community Join our discord server and ask guest directly or discuss related topics with the community. https://discord.gg/5Pj446VKNU ## 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
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