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0

$begingroup$

I was recently reading the paper Aggregated Residual Transformations for Deep Neural Networks.

One thing the author mentions in Section (5.1) is that increasing the cardinality (or, the number of branches), decreases validation error more than increasing the bottleneck width or increasing the depth. I understand the depth part, but I'm a bit confused about the width. Isn't the cardinality of a residual block the same as the bottleneck width? If not, what is the difference?

Thanks!

neural-network deep-learning convolution

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asked Mar 18 at 20:48

JohnDoeJohnDoe

1011

New contributor

JohnDoe is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

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add a comment | 

0

$begingroup$

I was recently reading the paper Aggregated Residual Transformations for Deep Neural Networks.

One thing the author mentions in Section (5.1) is that increasing the cardinality (or, the number of branches), decreases validation error more than increasing the bottleneck width or increasing the depth. I understand the depth part, but I'm a bit confused about the width. Isn't the cardinality of a residual block the same as the bottleneck width? If not, what is the difference?

Thanks!

neural-network deep-learning convolution

share|improve this question

asked Mar 18 at 20:48

JohnDoeJohnDoe

1011

New contributor

JohnDoe is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

add a comment | 

$begingroup$

I was recently reading the paper Aggregated Residual Transformations for Deep Neural Networks.

One thing the author mentions in Section (5.1) is that increasing the cardinality (or, the number of branches), decreases validation error more than increasing the bottleneck width or increasing the depth. I understand the depth part, but I'm a bit confused about the width. Isn't the cardinality of a residual block the same as the bottleneck width? If not, what is the difference?

Thanks!

neural-network deep-learning convolution

share|improve this question

asked Mar 18 at 20:48

JohnDoeJohnDoe

1011

New contributor

JohnDoe is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

share|improve this question

asked Mar 18 at 20:48

JohnDoeJohnDoe

1011

New contributor

JohnDoe is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share|improve this question

asked Mar 18 at 20:48

JohnDoeJohnDoe

1011

New contributor

JohnDoe is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked Mar 18 at 20:48

JohnDoeJohnDoe

1011

New contributor

JohnDoe is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked Mar 18 at 20:48

JohnDoeJohnDoe

1011