Rethinking Interpretation: Input-Agnostic Saliency Mapping of Deep Visual Classifiers

Naveed Akhtar, Mohammad Amir Asim Khan Jalwana

Research output: Chapter in Book/Conference paperConference paperpeer-review

3 Citations (Scopus)

Abstract

Saliency methods provide post-hoc model interpretation by attributing input features to the model outputs. Current methods mainly achieve this using a single input sample, thereby failing to answer input-independent inquiries about the model. We also show that input-specific saliency mapping is intrinsically susceptible to misleading feature attribution. Current attempts to use ‘general’ input features for model interpretation assume access to a dataset containing those features, which biases the interpretation. Addressing the gap, we introduce a new perspective of input-agnostic saliency mapping that computationally estimates the high-level features attributed by the model to its outputs. These features are geometrically correlated, and are computed by accumulating model’s gradient information with respect to an unrestricted data distribution. To compute these features, we nudge independent data points over the model loss surface towards the local minima associated by a human-understandable concept, e.g., class label for classifiers. With a systematic projection, scaling and refinement process, this information is transformed into an interpretable visualization without compromising its model-fidelity. The visualization serves as a standalone qualitative interpretation. With an extensive evaluation, we not only demonstrate successful visualizations for a variety of concepts for large-scale models, but also showcase an interesting utility of this new form of saliency mapping by identifying backdoor signatures in compromised classifiers.

Original languageEnglish
Title of host publicationAAAI-23 Technical Tracks 1
EditorsBrian Williams, Yiling Chen, Jennifer Neville
PublisherAAAI Press
Pages178-186
Number of pages9
Volume37
ISBN (Electronic)9781577358800
Publication statusPublished - 27 Jun 2023
Event37th AAAI Conference on Artificial Intelligence - Washington, United States
Duration: 7 Feb 202314 Feb 2023

Publication series

NameProceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023
Volume37

Conference

Conference37th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2023
Country/TerritoryUnited States
CityWashington
Period7/02/2314/02/23

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