Pith. sign in

Paper Citation Record · LEDGER

How explainable are adversarially-robust CNNs?

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2205.13042.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2205.13042 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:54:37.003248Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T16:39:26.663455Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 50967a3c-a44d-4826-a32f-2efdd06471c8 · inbound

Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts cites this paper.

Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts How explainable are adversarially-robust CNNs?

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-06T17:54:37.003248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:54:37.003248Z digest=sha256:709339b4eec53cae3915e315efd1691233ba4222815bc1f42f58a1a8dbb201a0

Observation 2aa9e4c9-268a-4297-b08b-b65b800a2a4d · inbound

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability cites this paper.

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability How explainable are adversarially-robust CNNs?

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-08-05T16:39:26.677229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T16:39:26.304491Z digest=sha256:69a71d3b21499c67cdc0a4bcd502b9a64a3b62229b5fd43e70cbedcfc9ebbd23