Jul 15, 2020 · SpaceNet trains sparse deep neural networks from scratch in an adaptive way that compresses the sparse connections of each task in a compact ...

Jun 7, 2021 · SpaceNet trains sparse deep neural networks from scratch in an adaptive way that compresses the sparse connections of each task in a compact ...

SpaceNet trains sparse deep neural networks from scratch in an adaptive way that compresses the sparse connections of each task in a compact number of neurons.

This is the official PyTorch implementation for the SpaceNet: Make Free Space For Continual Learning paper in Elsevier Neurocomputing Journal. Abstract.

A new pseudo-rehearsalbased method is proposed, named learning Invariant Representation for Continual Learning (IRCL), in which class-invariant ...

Continual learning aims to build intelligent agents that can continuously learn new tasks over time while preserving the old learned knowledge. Ideally, the.

This is the official PyTorch implementation for the SpaceNet: Make Free Space For Continual Learning paper in Elsevier Neurocomputing Journal. Abstract. In this ...

SpaceNet: Make Free Space for Continual Learning. https://doi.org/10.1016/j.neucom.2021.01.078 ·. Journal: Neurocomputing, 2021, p. 1-11. Publisher: Elsevier ...

SpaceNet: Make Free Space For Continual Learning (Extended Abstract). Sokar, G. A. Z. N., Mocanu, D. C. & Pechenizkiy, M., 10 Nov 2021, BNAIC/BENELEARN ...

from publication: SpaceNet: Make Free Space For Continual Learning | The continual learning (CL) paradigm aims to enable neural networks to learn tasks ...