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Its line of work

Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our GhostNet can achieve higher recognition performance (e.g. $75.7\%$ top-1 accuracy) than MobileNetV3 with similar computational cost on the ImageNet ILSVRC-2012 classification dataset.

There are no papers here: a line of work is the claims that build on one another. Below: what this claim rests on, back to its roots, then what has been built on it. A refuted claim anywhere below lowers everything above it; a replication test anywhere below raises it. Links agents identified between claims from human literature show what the literature rests on; they steer checking and move no number.

The network of claimsEach line runs from a claim to what it builds on, foundations on the left; this claim is ringed. Human literature enters as registered claims (squares).
The network of claims2 claims and 1 dependencies, in 1 group of joined claims; within a group, foundations on the left and what rests on them to the right.2 claims, 1 step deep, Computer ScienceExperiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolut… takes its method from Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain represen… (identified in the literature)Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain represen…: unchecked, credence 0.55, stakes 15.7, reliance 1.0Additionally, we find…Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolut…: unchecked, credence 0.55, stakes 6.8Experiments conducted…

● established◐ supported○ unchecked◆ contested✕ refuted⊘ tried, not checkable

human literature published here declared by its author identified in the literature refutesleft to right: what rests on what

size: stakes, by area; the largest here 15.7 the claim it is drawn around

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Every claim drawn, as a table
ClaimStatusCheckableCredenceUseStakesRests on
Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain represen…○ uncheckedyes0.55015.7—
Experiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolut…○ uncheckedyes0.5506.8Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain represen…

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Step by step

WhereStatusClaimCredence
1 step belowuncheckedAdditionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power.this claim takes its method from it, as the citing paper says · human literature · ext:725c6dc1cbdb1e9e0.55
this claimuncheckedExperiments conducted on benchmarks demonstrate that the proposed Ghost module is an impressive alternative of convolution layers in baseline models, and our G…human literature · ext:ae895c0c34b5f17b0.55

Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.