What is recorded here?
Uses trained split-data classifiers and Bayesian policy exploration to find augmentation policies whose transformed examples match a learned data distribution, then retrains on the augmented full training set.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Reduced search cost is not absence of search or final model training; search-time comparisons are hardware/setting dependent. First two authors contribute equally.
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People and systems
Sungbin Lim; Ildoo Kim; Taesup Kim; Chiheon Kim; Sungwoong Kim
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Fast AutoAugment density-matching policy search
Uses trained split-data classifiers and Bayesian policy exploration to find augmentation policies whose transformed examples match a learned data distribution, then retrains on the augmented full training set.
Scientific scope
Reduced search cost is not absence of search or final model training; search-time comparisons are hardware/setting dependent. First two authors contribute equally.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Sungbin Lim; Ildoo Kim; Taesup Kim; Chiheon Kim; Sungwoong Kim
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.