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Memory-Efficient Incremental Learning Through Feature Adaptation

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12361)

Abstract

We introduce an approach for incremental learning that preserves feature descriptors of training images from previously learned classes, instead of the images themselves, unlike most existing work. Keeping the much lower-dimensional feature embeddings of images reduces the memory footprint significantly. We assume that the model is updated incrementally for new classes as new data becomes available sequentially. This requires adapting the previously stored feature vectors to the updated feature space without having access to the corresponding original training images. Feature adaptation is learned with a multi-layer perceptron, which is trained on feature pairs corresponding to the outputs of the original and updated network on a training image. We validate experimentally that such a transformation generalizes well to the features of the previous set of classes, and maps features to a discriminative subspace in the feature space. As a result, the classifier is optimized jointly over new and old classes without requiring old class images. Experimental results show that our method achieves state-of-the-art classification accuracy in incremental learning benchmarks, while having at least an order of magnitude lower memory footprint compared to image-preserving strategies.

Notes

Acknowledgements

This research was funded in part by NSF grants IIS 1563727 and IIS 1718221, Google Research Award, Amazon Research Award, and AWS Machine Learning Research Award.

Supplementary material

504471_1_En_41_MOESM1_ESM.pdf (388 kb)
Supplementary material 1 (pdf 388 KB)

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Google ResearchMeylanFrance
  2. 2.University of Illinois at Urbana-ChampaignChampaignUSA

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