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DARTS

Differentiable Architecture Search
Hanxiao Liu, Karen Simonyan, Yiming Yang
arXiv: https://arxiv.org/abs/1806.09055
Published as a conference paper at ICLR 2019
Masashi Shibata
NAS


https://tech.mercari.com/entry/2019/05/10/120000
(ex: 3000 GPU days)
RL (+ RNN)

Evolution algorithms

Bayesian optimization
GPU days
Cell
Weight sharing
• → One-shot Neural Architecture Search
Discrete domain
Continuous Relaxation 

Continuous Relaxation
• 

DAG 

• 

( : conv_3x3, max_pool, )

• skip connections multiple branches
Directed Acyclic Graph
Discrete domain → Continuous domain
Continuous Relaxation
Replace after the end of search
o(i, j) α softmax
o(i, j)
α
α(i, j)
→ bilevel optimization
w α
w validation data α
α training data w
w α
w validation data α
α training data w
α α w*(α)
w α
w validation data α
α training data w
Hessian
Cell (Block)
Cell (Block) 

• (skip
connections cell )

• repeating building block useful
design principle
(ex: RNN)
Cell (Block)
Neural Architecture Search: A Survey 

(arXiv: https://arxiv.org/abs/1808.05377)
1
2
3
4
We assume the cell to have two input
nodes and a single output node.
normal cell
normal cell
normal cell
normal cell
reduction cell
input tensor
preprocess0 preprocess1
Cells located at the 1/3 and 2/3 of the total
depth of the network are reduction cells.
DARTS: Differentiable Architecture Search at 社内論文読み会
• Cell : 8

• Cell Node : 7

• Operation : 8 (zero operation )

• 3×3 and 5×5 separable convolutions

• 3×3 and 5×5 dilated separable convolutions

• 3×3 max pooling, 3×3 average pooling

• identity, and zero.

•
•
CIFAR-10
DARTS: Differentiable Architecture Search at 社内論文読み会
DARTS: Differentiable Architecture Search at 社内論文読み会
DARTS: Differentiable Architecture Search at 社内論文読み会
DARTS: Differentiable Architecture Search at 社内論文読み会
Continuous Relaxation
• SoTA
(RobustDARTS, ASNG-NAS)
THANK YOU

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