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Table 1 Summary of architectures with different levels of reversibility

From: Reversible designs for extreme memory cost reduction of CNN training

Model

Accuracy

#Params

Channels

Pooling

\(M_{\theta }\)

\(M_{z}'+M_{g}'\)

\(\mathcal {M}\)

Resnet

\(94.7\%\)

3.1M

\(32 - 64 - 128 - 256\)

Max Pooling

12.5M

1928

1.01G

RevNet

\(94.5\%\)

3.1M

\(40 - 80 - 256 - 320\)

Max Pooling

12.7M

640

348M

i-RevNet

\(93.8\%\)

42.8M

\(32 - 128 - 512 - 2048\)

\(\mathcal {P}_c - \mathcal {P}_c - \mathcal {P}_c\)

171M

640

500M

RevNeXt (ours)

\(93.3\%\)

3.7M

\(32 - 128 - 512 - 512\)

\([\mathcal {P}_c, \mathcal {P}_c, \mathcal {P}_b]\)

14.8M

352

200M