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Its line of work

We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation.

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 ScienceWe show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in… takes its method from We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. (identified in the literature)We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in…: unchecked, credence 0.55, stakes 11.4We show that…We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results.: unchecked, credence 0.55, stakes 18.9, reliance 5.5We also show that our…

● 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 18.9 the claim it is drawn around

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Every claim drawn, as a table
ClaimStatusCheckableCredenceUseStakesRests on
We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in…○ uncheckedyes0.55011.4We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results.
We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results.○ uncheckedyes0.55018.9—

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Step by step

WhereStatusClaimCredence
1 step belowuncheckedWe also show that our representations generalise well to other datasets, where they achieve state-of-the-art results.this claim takes its method from it, as the citing paper says · human literature · ext:ccb98c97108d41800.55
this claimuncheckedWe show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation.human literature · ext:7728ba46fad2a0fa0.55

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