# KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training Source: [KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training]() Collection index: [llms.txt]() Data updated: 2025-12-24T19:49:40.591Z Authors: Truong Thao Nguyen, Balazs Gerofi, Edgar Josafat Martinez-Noriega, Fran\\c{c}ois Trahay, Mohamed Wahib Published: 2023-10-16T06:19:29.000Z ## Abstract This paper proposes a method for hiding the least-important samples during the training of deep neural networks to increase efficiency, i.e., to reduce the cost of training. Using information about the loss and prediction confidence during training, we adaptively find samples to exclude in a given epoch based on their contribution to the overall learning process, without significantly degrading accuracy. We explore the converge properties when accounting for the reduction in the number of SGD updates. Empirical results on various large-scale datasets and models used directly in image classification and segmentation show that while the with-replacement importance sampling algorithm performs poorly on large datasets, our method can reduce total training time by up to 22% impacting accuracy only by 0.4% compared to the baseline. Code available at https://github.com/TruongThaoNguyen/kakurenbo ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()