Image classification optimization technology based on differentiable neural architecture search improvement model [PDF]
PLoS ONEImage classification, as the core task of computer vision, has broad application value in fields such as medical diagnosis and intelligent transportation.However, the ability of differentiable neural architecture to search (NAS) for local information is ...
Yuxuan Ji, Wenshu Li, Nan Yu
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EEG-Based Emotion Estimation Model Integrating Structural and Time-Series Information Based on Deep Learning Architecture Optimization [PDF]
SensorsEmotion recognition is increasingly important for applications in mental health and personalized marketing. Traditional methods based on facial and vocal cues lack robustness due to voluntary control, motivating the use of EEG signals that capture neural
Kota Tsuji, Keiko Ono, Takuya Futagami
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Differentiable Neural Architecture Search Method for Blind Image Deblurring [PDF]
Jisuanji gongcheng, 2021 The design of neural networks for image deblurring requires heavy work of manual parameter tuning. To address the problem, a differentiable neural network search method for image deblurring is proposed.
MIAO Si, ZHU Yongxin
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A Fast and Progressive Convolutional Neural Architecture Search Algorithm [PDF]
Jisuanji gongcheng, 2022 Manually designing a Convolutional Neural Network(CNN) architecture is extremely difficult and requires a high level of professionalism.The gradient differentiable search is fast and efficient.However, this method has some drawbacks, such as a large gap ...
ZHAO Liang, FANG Wei
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No-reference image quality assessment based on automatic machine learning [PDF]
ITM Web of Conferences, 2022 In different applications in deep learning, due to different required features, it is necessary to design specialized Neural Network structure. However, the design of the structure largely depends on the relevant subject knowledge of researchers and lots
Qian Qi, Sang Qingbing
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Differentiable Neural Architecture, Mixed Precision and Accelerator Co-Search
IEEE Access, 2023 Quantization, effective Neural Network architecture, and efficient accelerator hardware are three important design paradigms to maximize accuracy and efficiency.
Krishna Teja Chitty-Venkata +4 more
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Differentiable Architecture Search Based on Coordinate Descent
IEEE Access, 2021 Neural architecture search (NAS) is an automated method searching for the optimal network architecture by optimizing the combinations of edges and operations.
Pyunghwan Ahn +2 more
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Differentiable Neural Architecture Search for High-Dimensional Time Series Forecasting
IEEE Access, 2021 This study applies neural architecture search (NAS) techniques to the modeling of high-dimensional time series data such as multi-variate stock indices.
Renlong Jie, Junbin Gao
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RARTS: An Efficient First-Order Relaxed Architecture Search Method
IEEE Access, 2022 Differentiable architecture search (DARTS) is an effective method for data-driven neural network design based on solving a bilevel optimization problem.
Fanghui Xue, Yingyong Qi, Jack Xin
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Exploring Neural Architecture Search Space via Deep Deterministic Sampling
IEEE Access, 2021 Recent developments in Neural Architecture Search (NAS) resort to training the supernet of a predefined search space with weight sharing to speed up architecture evaluation.
Keith G. Mills +8 more
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