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Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations

Summary: Occlusion-based CNN explanations reframed as incremental view maintenance; algebraic framework with materialized views and multi-query optimization to reuse computation across inferences. Krypton prototype (CPU/GPU) achieves up to 5× exact and 35× approximate speedups using novel approximate inference techniques that exploit CNN semantics. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5748
Venue
SIGMOD
Year
2019
Pagerank
7.8907997e-05
Overall Rank
2,987 | 79.51%
DOI
10.1145/3299869.3319874

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{nakandala_sigmod19,
        title = {{Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations}},
        author = {Nakandala, Supun and Kumar, Arun and Papakonstantinou, Yannis},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3319874},
        url = {https://dl.acm.org/doi/10.1145/3299869.3319874},
        year = {2019}
}

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