For unseen ImageNet categories only, MLP-SGCZ slightly improves over the authors' GCNZ at both 2-hop and 3-hop accuracy with 4.3M versus 9.5M parameters, but remains below EXEM 1-nns and ADGPM, which use stronger visual features.

For unseen ImageNet categories only, MLP-SGCZ slightly improves over the authors' GCNZ at both 2-hop and 3-hop accuracy with 4.3M versus 9.5M parameters, but remains below EXEM 1-nns and ADGPM, which use stronger visual features. · CiteArk

Measurement results

0/13 assessed

0 supported0 disputed or conflicting13 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Metric and conditions	Reported	Latest observation	Evidence judgment
	27%	—	Not assessed
	7.1%	—	Not assessed
	26.6%	—	Not assessed
	6.3%	—	Not assessed
	19.8%	—	Not assessed
	4.1%	—	Not assessed
reportedPlusMinus: 0.2	20.9%	—	Not assessed
reportedPlusMinus: 0	4.3%	—	Not assessed
reportedPlusMinus: 0.2	21.2%	—	Not assessed
reportedPlusMinus: 0.1	4.4%	—	Not assessed
	9.5 millions of parameters	—	Not assessed
	4.3 millions of parameters	—	Not assessed

For unseen ImageNet categories only, MLP-SGCZ slightly improves over the authors' GCNZ at both 2-hop and 3-hop accuracy with 4.3M versus 9.5M parameters, but remains below EXEM 1-nns and ADGPM, which use stronger visual features.

Reported and observed measurements

Assessments (0)

Runs (0)

Measurement results