Demonstration of Krypton: Optimized CNN Inference for Occlusion-based Deep CNN Explanations

Summary: Krypton optimizes occlusion-based CNN explanations by incremental/approximate inference, cutting runtime up to 35x. Leverages classic query-optimization ideas to enable interactive diagnosis of CNN predictions in radiology and natural images. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 11894
Venue: VLDB
Year: 2019
Pagerank: -
Overall Rank: 13,313 | 7.39%
DOI: 10.14778/3352063.3352093

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
683	Cerebro: A Data System for Optimized Deep Learning Model Selection	2020	VLDB	0.00018195476
8,864	Cerebro: A Layered Data Platform for Scalable Deep Learning	2021	CIDR	4.4326439e-05
13,171	Reimagining Deep Learning Systems Through the Lens of Data Systems	2024	VLDB	-

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2,863	Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations	2019	SIGMOD	7.9877991e-05

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