Claims › ext:3ecd4da9688b2bf1 › line of work
Its line of work
The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.
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.
● 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 14.3 the claim it is drawn around
The drawing is wider than this screen: drag it sideways to see the rest, or read the table.
Every claim drawn, as a table
| Claim | Status | Checkable | Credence | Use | Stakes | Rests on |
|---|---|---|---|---|---|---|
| MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight de… | ○ unchecked | yes | 0.55 | 0 | 14.3 | — |
| The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated… | ○ unchecked | yes | 0.55 | 0 | 12.0 | MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight de… |
See its whole group in the network, where it can be filtered and sized.
Step by step
| Where | Status | Claim | Credence |
|---|---|---|---|
| 1 step below | unchecked | MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks.this claim takes its method from it, as the citing paper says · human literature · ext:245914e3c3cc70ff | 0.55 |
| this claim | unchecked | The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO det…human literature · ext:3ecd4da9688b2bf1 | 0.55 |
Background mentions carry no weight and are not part of the line. Every number recomputes from the public log.