Pith. sign in

REVIEW

Symbiotic Hybrid Neural Network Watchdog For Outlier Detection

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2103.00582 v2 pith:6IXH3W4I submitted 2021-02-28 cs.LG eess.IV

classification cs.LGeess.IV
keywords neuralnetworkwatchdogclassifynetworkssymbioticallowingattached
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Neural networks are largely black boxes. A neural network trained to classify fruit may classify a picture of a giraffe as a banana. A neural network watchdog's job is to identify such inputs, allowing a classifier to disregard such data. We investigate whether the watchdog should be separate from the neural network or symbiotically attached. We present empirical evidence that the symbiotic watchdog performs better than when the neural networks are disjoint.

Discussion (0). Continue with ORCID to comment.

Pith tools