# Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification Source: [Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:37:22.464Z Authors: Youngkee Kim, Won Joon Yun, Youn Kyu Lee, Soyi Jung, Joongheon Kim Published: 2022-02-17T11:36:43.000Z ## Abstract In many deep neural network (DNN) applications, the difficulty of gathering high-quality data in the industry field hinders the practical use of DNN. Thus, the concept of transfer learning has emerged, which leverages the pretrained knowledge of DNNs trained on large-scale datasets. Therefore, this paper suggests two-stage architectural fine-tuning, inspired by neural architecture search (NAS). One of main ideas is mutation, which reduces the search cost using given architectural information. Moreover, early-stopping is considered which cuts NAS costs by terminating the search process in advance. Experimental results verify our proposed method reduces 32.4% computational and 22.3% searching costs. ## 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]()