TinySubNets combines per-layer pruning, adaptive quantization, and KL-gated weight sharing to run continual image classification with much lower memory than PackNet, WSN, and Ada-QPacknet.
M.; New, A.; Aguilar-Simon, M.; Al-Halah, Z.; Arnold, S
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TinySubNets: An efficient and low capacity continual learning strategy
TinySubNets combines per-layer pruning, adaptive quantization, and KL-gated weight sharing to run continual image classification with much lower memory than PackNet, WSN, and Ada-QPacknet.