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)
Incoming Non-self Citations Over Time
Authors
- 1. Supun Nakandala (University of California San Diego)
- 2. Arun Kumar (University of California San Diego)
- 3. Yannis Papakonstantinou (University of California San Diego)
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}
}
Incoming Citations (Sorted by Pagerank)
Showing 13 of 13 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 69 | Answering Queries Using Views (Extended Abstract) | 1995 | PODS | 0.00038090878 |
| 284 | NoScope: Optimizing Neural Network Queries over Video at Scale | 2017 | VLDB | 0.00022370521 |
| 363 | Approximate Query Processing: Taming the TeraBytes! A Tutorial | 2001 | VLDB | 0.0002005475 |
| 2,059 | LINVIEW: Incremental View Maintenance for Complex Analytical Queries | 2014 | SIGMOD | 9.2471145e-05 |
| 4,406 | Incremental View Maintenance over Array Data | 2017 | SIGMOD | 6.7191598e-05 |
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