SEDONA: Search for Decoupled Neural Networks toward Greedy Block-wise Learning

Myeongjang Pyeon, Jihwan Moon, Taeyoung Hahn, Gunhee Kim

Keywords: AutoML, Neural Architecture Search, Greedy Learning, Deep Learning

Abstract: Backward locking and update locking are well-known sources of inefficiency in backpropagation that prevent from concurrently updating layers. Several works have recently suggested using local error signals to train network blocks asynchronously to overcome these limitations. However, they often require numerous iterations of trial-and-error to find the best configuration for local training, including how to decouple network blocks and which auxiliary networks to use for each block. In this work, we propose a differentiable search algorithm named SEDONA to automate this process. Experimental results show that our algorithm can consistently discover transferable decoupled architectures for VGG and ResNet variants, and significantly outperforms the ones trained with end-to-end backpropagation and other state-of-the-art greedy-leaning methods in CIFAR-10, Tiny-ImageNet and ImageNet.

One-sentence Summary: Our approach is the first attempt to automate decoupling neural networks for greedy block-wise learning and outperforms both end-to-end backprop and state-of-the-art greedy-learning methods on CIFAR-10, Tiny-ImageNet and ImageNet classification.

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Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [ImageNet](https://paperswithcode.com/dataset/imagenet)