Equation 8 · A History of Small and On-Device AI
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With the near-universal 33 kernel, that ratio works out to roughly an eighth to a ninth of the original computation, for what the paper reports as a small accuracy cost [ 1 ] . MobileNet then added two further dials rather than shipping one fixed shape: a width multiplier that uniformly thins the number of channels at every layer, and a resolution multiplier that shrinks the input image, letting a developer choose a point on an accuracy-versus-latency curve instead of accepting whatever a single published checkpoint offered [ 1 ] . That is the detail worth keeping from this stage of the history: SqueezeNet showed that a deliberately designed structure could match a much larger…
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With the near-universal 33 kernel, that ratio works out to roughly an eighth to a ninth of the original computation, for what the paper reports as a small accuracy cost [ 1 ] . MobileNet then added two further dials rather than shipping one fixed shape: a width multiplier that uniformly thins the number of channels at every layer, and a resolution multiplier that shrinks the input image, letting a developer choose a point on an accuracy-versus-latency curve instead of accepting whatever a single published checkpoint offered [ 1 ] . That is the detail worth keeping from this stage of the history: SqueezeNet showed that a deliberately designed structure could match a much larger network’s accuracy at a fraction of the parameters; MobileNet turned the same insight into a convolution that could be dropped into any architecture, with knobs that let one trained design be retargeted across a whole range of phones rather than retrained for each.
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