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Abstract #0016

Harmonization for a black-box deep learning model

Minjun Kim1, Hwihun Jeong1, Hoigi Seo1, Wongi Jeong1, Juhyung Park1, Se Young Chun1, and Jongho Lee1
1Seoul National University, Seoul, Korea, Republic of

Synopsis

Keywords: Analysis/Processing, Reproductive, Data Processing, Analysis/Processing, AI/ML Image Reconstruction

Motivation: Most commercially available deep-learning tools are black-box models, where additional training is not possible. This may hamper the performance for unseen-data. This work proposes novel harmonization concept for this black-box model.

Goal(s): We propose a harmonization pipeline, BboxHarmony, that trains a harmonization network for a black-box model.

Approach: First, randomly perturbed images were evaluated for the black-box model and the images with high performance results (pseudo-target domain images) were used to train the harmonization network. Then, the network was refined for black-box model using zeroth-order optimization that approximates backpropagation.

Results: BboxHarmony successfully created harmonized images that provided high performance for the black-box model.

Impact: BboxHarmony proposes a novel concept of harmonizing data for a black-box model and may have an important impact in the real-world where most commercial networks are black-box.

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Keywords