AutoML | Literature on Neural Architecture Search

@article{OCAL2025107475,

title = {Network-aware federated neural architecture search},

author = {Göktuğ Öcal and Atay Özgövde},

url = {https://www.sciencedirect.com/science/article/pii/S0167739X24004205},

doi = {https://doi.org/10.1016/j.future.2024.07.053},

issn = {0167-739X},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Future Generation Computer Systems},

volume = {162},

pages = {107475},

abstract = {The cooperation between Deep Learning (DL) and edge devices has further advanced technological developments, allowing smart devices to serve as both data sources and endpoints for DL-powered applications. However, the success of DL relies on optimal Deep Neural Network (DNN) architectures, and manually developing such systems requires extensive expertise and time. Neural Architecture Search (NAS) has emerged to automate the search for the best-performing neural architectures. Meanwhile, Federated Learning (FL) addresses data privacy concerns by enabling collaborative model development without exchanging the private data of clients. In a FL system, network limitations can lead to biased model training, slower convergence, and increased communication overhead. On the other hand, traditional DNN architecture design, emphasizing validation accuracy, often overlooks computational efficiency and size constraints of edge devices. This research aims to develop a comprehensive framework that effectively balances trade-offs between model performance, communication efficiency, and the incorporation of FL into an iterative NAS algorithm. This framework aims to overcome challenges by addressing the specific requirements of FL, optimizing DNNs through NAS, and ensuring computational efficiency while considering the network constraints of edge devices. To address these challenges, we introduce Network-Aware Federated Neural Architecture Search (NAFNAS), an open-source federated neural network pruning framework with network emulation support. Through comprehensive testing, we demonstrate the feasibility of our approach, efficiently reducing DNN size and mitigating communication challenges. Additionally, we propose Network and Distribution Aware Client Grouping (NetDAG), a novel client grouping algorithm tailored for FL with diverse DNN architectures, considerably enhancing efficiency of communication rounds and update balance.},

keywords = {},

pubstate = {published},

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}

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4317.

Rahman, Abdur; Street, Jason; Wooten, James; Marufuzzaman, Mohammad; Gude, Veera G.; Buchanan, Randy; Wang, Haifeng

MoistNet: Machine vision-based deep learning models for wood chip moisture content measurement Journal Article

In: Expert Systems with Applications, vol. 259, pp. 125363, 2025, ISSN: 0957-4174.

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4316.

Jiang, Zhiying; Liu, Risheng; Yang, Shuzhou; Zhang, Zengxi; Fan, Xin

DRNet: Learning a dynamic recursion network for chaotic rain streak removal Journal Article

In: Pattern Recognition, vol. 158, pp. 111004, 2025, ISSN: 0031-3203.

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4315.

Wu, Zhenpeng; Chen, Jiamin; Al-Sabri, Raeed; Oloulade, Babatounde Moctard; Gao, Jianliang

Asymmetric augmented paradigm-based graph neural architecture search Journal Article

In: Information Processing & Management, vol. 62, no. 1, pp. 103897, 2025, ISSN: 0306-4573.

Abstract | Links | BibTeX

@article{WU2025103897,

title = {Asymmetric augmented paradigm-based graph neural architecture search},

author = {Zhenpeng Wu and Jiamin Chen and Raeed Al-Sabri and Babatounde Moctard Oloulade and Jianliang Gao},

url = {https://www.sciencedirect.com/science/article/pii/S0306457324002565},

doi = {https://doi.org/10.1016/j.ipm.2024.103897},

issn = {0306-4573},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Information Processing & Management},

volume = {62},

number = {1},

pages = {103897},

abstract = {In most scenarios of graph-based tasks, graph neural networks (GNNs) are trained end-to-end with labeled samples. Labeling graph samples, a time-consuming and expert-dependent process, leads to huge costs. Graph data augmentations can provide a promising method to expand labeled samples cheaply. However, graph data augmentations will damage the capacity of GNNs to distinguish non-isomorphic graphs during the supervised graph representation learning process. How to utilize graph data augmentations to expand labeled samples while preserving the capacity of GNNs to distinguish non-isomorphic graphs is a challenging research problem. To address the above problem, we abstract a novel asymmetric augmented paradigm in this paper and theoretically prove that it offers a principled approach. The asymmetric augmented paradigm can preserve the capacity of GNNs to distinguish non-isomorphic graphs while utilizing augmented labeled samples to improve the generalization capacity of GNNs. To be specific, the asymmetric augmented paradigm will utilize similar yet distinct asymmetric weights to classify the real sample and augmented sample, respectively. To systemically explore the benefits of asymmetric augmented paradigm under different GNN architectures, rather than studying individual asymmetric augmented GNN (A2GNN) instance, we then develop an auto-search engine called Asymmetric Augmented Graph Neural Architecture Search (A2GNAS) to save human efforts. We empirically validate our asymmetric augmented paradigm on multiple graph classification benchmarks, and demonstrate that representative A2GNN instances automatically discovered by our A2GNAS method achieve state-of-the-art performance compared with competitive baselines. Our codes are available at: https://github.com/csubigdata-Organization/A2GNAS.},

keywords = {},

pubstate = {published},

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4314.

Wang, Weibo; Li, Hua

NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search Journal Article

In: Biomedical Signal Processing and Control, vol. 100, pp. 107022, 2025, ISSN: 1746-8094.

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@article{WANG2025107022,

title = {NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search},

author = {Weibo Wang and Hua Li},

url = {https://www.sciencedirect.com/science/article/pii/S1746809424010802},

doi = {https://doi.org/10.1016/j.bspc.2024.107022},

issn = {1746-8094},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Biomedical Signal Processing and Control},

volume = {100},

pages = {107022},

abstract = {In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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4313.

Solis-Martin, David; Galan-Paez, Juan; Borrego-Diaz, Joaquin

Bayesian Model Selection Pruning in Predictive Maintenance Proceedings Article

In: Quintián, Héctor; Corchado, Emilio; Lora, Alicia Troncoso; García, Hilde Pérez; Pérez, Esteban Jove; Rolle, José Luis Calvo; de Pisón, Francisco Javier Martínez; Bringas, Pablo García; Álvarez, Francisco Martínez; Herrero, Álvaro; Fosci, Paolo (Ed.): Hybrid Artificial Intelligent Systems, pp. 263–274, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-74183-8.

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4312.

Solis-Martin, David; Galan-Paez, Juan; Borrego-Diaz, Joaquin

Bayesian Model Selection Pruning in Predictive Maintenance Proceedings Article

In: Quintián, Héctor; Corchado, Emilio; Lora, Alicia Troncoso; García, Hilde Pérez; Pérez, Esteban Jove; Rolle, José Luis Calvo; de Pisón, Francisco Javier Martínez; Bringas, Pablo García; Álvarez, Francisco Martínez; Herrero, Álvaro; Fosci, Paolo (Ed.): Hybrid Artificial Intelligent Systems, pp. 263–274, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-74183-8.

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4311.

Garcia-Garcia, Cosijopii; Derbel, Bilel; Morales-Reyes, Alicia; Escalante, Hugo Jair

Speeding up the Multi-objective NAS Through Incremental Learning Proceedings Article

In: Martínez-Villaseñor, Lourdes; Ochoa-Ruiz, Gilberto (Ed.): Advances in Soft Computing, pp. 3–15, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-75543-9.

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4310.

Liu, Wenbo; Deng, Tao; An, Rui; Yan, Fei

DARTS-CGW: Research on Differentiable Neural Architecture Search Algorithm Based on Coarse Gradient Weighting Proceedings Article

In: Lin, Zhouchen; Cheng, Ming-Ming; He, Ran; Ubul, Kurban; Silamu, Wushouer; Zha, Hongbin; Zhou, Jie; Liu, Cheng-Lin (Ed.): Pattern Recognition and Computer Vision, pp. 31–44, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-97-8502-5.

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@inproceedings{10.1007/978-981-97-8502-5_3,

title = {DARTS-CGW: Research on Differentiable Neural Architecture Search Algorithm Based on Coarse Gradient Weighting},

author = {Wenbo Liu and Tao Deng and Rui An and Fei Yan},

editor = {Zhouchen Lin and Ming-Ming Cheng and Ran He and Kurban Ubul and Wushouer Silamu and Hongbin Zha and Jie Zhou and Cheng-Lin Liu},

url = {https://link.springer.com/chapter/10.1007/978-981-97-8502-5_3},

isbn = {978-981-97-8502-5},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Pattern Recognition and Computer Vision},

pages = {31–44},

publisher = {Springer Nature Singapore},

address = {Singapore},

abstract = {Differential architecture search (DARTS) has emerged as a prominent research area, yet it grapples with a longstanding challenge: the discretization discrepancy problem. This issue directly impedes the search for an optimal model architecture and undermines search algorithm performance. To alleviate this issue, we propose a novel coarse gradient weighting algorithm. Our proposed algorithm has the capability to simulate the discretization process, wherein the architectural parameters move toward both ends. And we integrate this discretization process into the training phase of the architectural parameters, enabling the model to adapt to the discretization process in a trial-and-error fashion. Specifically, based on the architectural parameters in training, we divide the candidate operations into two regions, i.e., the easy-to-select region and the hard-to-be-selected region. The different weighting strategies are implemented in different regions so that the architectural parameters are pushed to the ends. The processed architecture parameters are used for training, which is equivalent to introducing the discretization process into the search phase. Additionally, we use the coarse gradient algorithm to optimize the updating process of the weighting algorithm and theoretically justify the rationality of the coarse gradient weighting algorithm. Extensive experimental results demonstrate that our proposed method can improve the performance of the searched model and make DARTS more robust without adding additional search time.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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4309.

Man, Wenxing; Xu, Liming; He, Chunlin

Evolutionary architecture search for generative adversarial networks using an aging mechanism-based strategy Journal Article

In: Neural Networks, vol. 181, pp. 106877, 2025, ISSN: 0893-6080.

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4308.

Zhong, Rui; Xu, Yuefeng; Zhang, Chao; Yu, Jun

Efficient multiplayer battle game optimizer for numerical optimization and adversarial robust neural architecture search Journal Article

In: Alexandria Engineering Journal, vol. 113, pp. 150-168, 2025, ISSN: 1110-0168.

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@article{ZHONG2025150,

title = {Efficient multiplayer battle game optimizer for numerical optimization and adversarial robust neural architecture search},

author = {Rui Zhong and Yuefeng Xu and Chao Zhang and Jun Yu},

url = {https://www.sciencedirect.com/science/article/pii/S1110016824014935},

doi = {https://doi.org/10.1016/j.aej.2024.11.035},

issn = {1110-0168},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Alexandria Engineering Journal},

volume = {113},

pages = {150-168},

abstract = {This paper introduces a novel metaheuristic algorithm, known as the efficient multiplayer battle game optimizer (EMBGO), specifically designed for addressing complex numerical optimization tasks. The motivation behind this research stems from the need to rectify identified shortcomings in the original MBGO, particularly in search operators during the movement phase, as revealed through ablation experiments. EMBGO mitigates these limitations by integrating the movement and battle phases to simplify the original optimization framework and improve search efficiency. Besides, two efficient search operators: differential mutation and Lévy flight are introduced to increase the diversity of the population. To evaluate the performance of EMBGO comprehensively and fairly, numerical experiments are conducted on benchmark functions such as CEC2017, CEC2020, and CEC2022, as well as engineering problems. Twelve well-established MA approaches serve as competitor algorithms for comparison. Furthermore, we apply the proposed EMBGO to the complex adversarial robust neural architecture search (ARNAS) tasks and explore its robustness and scalability. The experimental results and statistical analyses confirm the efficiency and effectiveness of EMBGO across various optimization tasks. As a potential optimization technique, EMBGO holds promise for diverse applications in real-world problems and deep learning scenarios. The source code of EMBGO is made available in https://github.com/RuiZhong961230/EMBGO.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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4307.

Wang, Yu; Fan, Jiahao; Sun, Yanan

Classification of sewer pipe defects based on an automatically designed convolutional neural network Journal Article

In: Expert Systems with Applications, vol. 264, pp. 125806, 2025, ISSN: 0957-4174.

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4306.

Fu, Wei; Lou, Wenqi; Qin, Yunji; Gong, Lei; Wang, Chao; Zhou, Xuehai

MFNAS: Multi-fidelity Exploration in Neural Architecture Search with Stable Zero-Shot Proxy Proceedings Article

In: Hadfi, Rafik; Anthony, Patricia; Sharma, Alok; Ito, Takayuki; Bai, Quan (Ed.): PRICAI 2024: Trends in Artificial Intelligence, pp. 348–360, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-0116-5.

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@inproceedings{10.1007/978-981-96-0116-5_29,

title = {MFNAS: Multi-fidelity Exploration in Neural Architecture Search with Stable Zero-Shot Proxy},

author = {Wei Fu and Wenqi Lou and Yunji Qin and Lei Gong and Chao Wang and Xuehai Zhou},

editor = {Rafik Hadfi and Patricia Anthony and Alok Sharma and Takayuki Ito and Quan Bai},

isbn = {978-981-96-0116-5},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {PRICAI 2024: Trends in Artificial Intelligence},

pages = {348–360},

publisher = {Springer Nature Singapore},

address = {Singapore},

abstract = {Neural architecture search (NAS) automates the design of neural networks for specific tasks. Recently, zero-shot NAS has attracted much attention. Unlike traditional NAS, which relies on training to rank architectures, zero-shot NAS uses gradients or activation information to evaluate architecture performance. However, existing zero-shot NAS methods are limited by their inconsistent architecture ranking and the evaluation bias of their search algorithm, making it challenging to discover networks with high accuracy efficiently. To address this dilemma, this paper proposes an efficient and stable search framework for zero-shot NAS. Firstly, we design a stable zero-shot proxy, which achieves good consistency with network accuracy by utilizing filtered gradient information. On this basis, we employ a multi-fidelity evolutionary algorithm for efficient exploration. This algorithm utilizes multi-fidelity proxies to correct the bias towards certain types of networks and enhances the ability to distinguish high-performing architectures, achieving rapid convergence through performance-directed multi-point crossover and mutation. Experimental results conducted on NATS-Bench demonstrate that our framework can discover high-performance architectures within minutes of GPU time, outperforming existing training-free and training-based NAS methods. The code is available at https://github.com/mine7777/MFNAS.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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4305.

Verma, Sahil; Kumar, Prabhat; Singh, Jyoti Prakash

MLP-GNAS: Meta-learning-based predictor-assisted Genetic Neural Architecture Search system Journal Article

In: Applied Soft Computing, vol. 169, pp. 112527, 2025, ISSN: 1568-4946.

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@article{VERMA2025112527,

title = {MLP-GNAS: Meta-learning-based predictor-assisted Genetic Neural Architecture Search system},

author = {Sahil Verma and Prabhat Kumar and Jyoti Prakash Singh},

url = {https://www.sciencedirect.com/science/article/pii/S1568494624013012},

doi = {https://doi.org/10.1016/j.asoc.2024.112527},

issn = {1568-4946},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Applied Soft Computing},

volume = {169},

pages = {112527},

abstract = {Convolutional neural networks achieve state-of-the-art results on many image classification tasks. However, according to the No Free Lunch Theorem, no single model performs optimally for all the datasets. Hence, the manual development of these models using the hit-and-trial approach requires high computational overhead. Transfer learning mitigates this issue to some extent by transferring knowledge learned from similar domain tasks to target tasks through model architecture and weights. Yet, the model remains designed for the source task, and using the same model on the target task may result in over-parameterization or sub-optimal results. Henceforth, the current work proposes a meta-learning-based predictor-assisted genetic Neural Architecture Search (MLP-GNAS) system to automate the model generation process for plant disease detection tasks to validate its efficacy in real-world scenarios. To this cause, the MLP-GNAS system employs a meta-learning component to recommend the top 3 suitable models for a target dataset, followed by a genetic algorithm-based NAS to fine-tune the recommended model. In addition, the proposed approach uses a CNN-based performance predictor designed to discard models unlikely to surpass the optimal performance recorded thus far. Further, extensive comparative experiments demonstrate that MLP-GNAS-generated models outperform 14 state-of-the-art models on two distinct plant disease datasets with an accuracy of 96.94% and 92.04%. Moreover, further comparison with the manually fine-tuned and NAS-generated models reveals that the proposed system outperforms both to obtain an accuracy of 98.35% and 99.95% on respective datasets.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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4304.

Oloulade, Babatounde Moctard; Gao, Jianliang; Chen, Jiamin; Al-Sabri, Raeed; Wu, Zhenpeng; Abdullah, Monir

Shapley-guided pruning for efficient graph neural architecture prediction in distributed learning environments Journal Article

In: Information Sciences, vol. 695, pp. 121695, 2025, ISSN: 0020-0255.

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4303.

Chen, Jiakun; Xu, Jie; Hu, Jiahui; Qiao, Liqiang; Wang, Shuo; Huang, Feiran; Li, Chaozhuo

Context-Aware Structural Adaptive Graph Neural Networks Proceedings Article

In: Hadfi, Rafik; Anthony, Patricia; Sharma, Alok; Ito, Takayuki; Bai, Quan (Ed.): PRICAI 2024: Trends in Artificial Intelligence, pp. 467–479, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-0116-5.

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4302.

Reddy, P. N. Aravinda; Ramachandra, Raghavendra; Rao, K. Sreenivasa; Mitra, Pabitra

NeuralMultiling: A Novel Neural Architecture Search for Smartphone Based Multilingual Speaker Verification Proceedings Article

In: Antonacopoulos, Apostolos; Chaudhuri, Subhasis; Chellappa, Rama; Liu, Cheng-Lin; Bhattacharya, Saumik; Pal, Umapada (Ed.): Pattern Recognition, pp. 406–423, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-78341-8.

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4301.

Li, Jie; Wang, Yuxia; Wang, Yifan; Yu, Ruiyun; Wang, Xingwei

HN-Darts:Hybrid Network Differentiable Architecture Search for Industrial Scenarios Proceedings Article

In: Hadfi, Rafik; Anthony, Patricia; Sharma, Alok; Ito, Takayuki; Bai, Quan (Ed.): PRICAI 2024: Trends in Artificial Intelligence, pp. 322–327, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-0116-5.

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4300.

Jin, Xiao; Yu, Wen; Chen, Dai-Wei; Shi, Wei

DFD-NAS: General deepfake detection via efficient neural architecture search Journal Article

In: Neurocomputing, vol. 619, pp. 129129, 2025, ISSN: 0925-2312.

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@article{JIN2025129129,

title = {DFD-NAS: General deepfake detection via efficient neural architecture search},

author = {Xiao Jin and Wen Yu and Dai-Wei Chen and Wei Shi},

url = {https://www.sciencedirect.com/science/article/pii/S0925231224019003},

doi = {https://doi.org/10.1016/j.neucom.2024.129129},

issn = {0925-2312},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Neurocomputing},

volume = {619},

pages = {129129},

abstract = {As the saying goes, “seeing is believing”. However, with the development of digital face editing tools, we can no longer trust what we can see. Though face forgery detection has made promising progress, most current methods are manually designed by human experts, which is labor-intensive. In this paper, we develop an end-to-end framework based on neural architecture search (NAS) for deepfake detection, which can automatically design network architectures without human intervention. First, a forgery-oriented search space is created to choose appropriate operations for this task, which facilitates the search process in finding the most valuable gradient information for face forgery detection. Second, inspired by the fact that the gap between training error and test error is a good indicator of generalization ability for a classification task, we propose a novel performance estimation metric. This metric encourages the error in the performance estimation phase to be close to the error in the search phase, which guides the search process to select more general models. The cross-dataset search is also considered to develop more general architectures. Eventually, we design a cell cascaded pyramid network (C2PN) for final detection, which aggregates multiscale features for performance improvements. Compared with state-of-the-art networks artificially designed, our method achieves competitive performance in both in-dataset and cross-dataset scenarios.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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4299.

Sarker, Prodip Kumar

Transformer-based neural architecture search for effective visible-infrared person re-identification Journal Article

In: Neurocomputing, vol. 620, pp. 129257, 2025, ISSN: 0925-2312.

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@article{SARKER2025129257,

title = {Transformer-based neural architecture search for effective visible-infrared person re-identification},

author = {Prodip Kumar Sarker},

url = {https://www.sciencedirect.com/science/article/pii/S0925231224020289},

doi = {https://doi.org/10.1016/j.neucom.2024.129257},

issn = {0925-2312},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Neurocomputing},

volume = {620},

pages = {129257},

abstract = {Visible-infrared person re-identification (VI-reID) is a complex task in security and video surveillance that aims to identify and match a person captured by various non-overlapping cameras. In recent years, there has been a notable advancement in reID owing to the development of transformer-based architectures. Although many existing methods emphasize on learning both modality-specific and shared features, challenges remain in fully exploiting the complementary information between infrared and visible modalities. Consequently, there is still opportunity to increase retrieval performance by effectively comprehending and integrating cross-modality semantic information. These designs often have problems with model complexity and time-consuming processes. To tackle these issues, we employ a novel transformer-based neural architecture search (TNAS) deep learning approach for effective VI-reID. To alleviate modality gaps, we first introduce a global–local transformer (GLT) module that captures features at both global and local levels across different modalities, contributing to better feature representation and matching. Then, an efficient neural architecture search (NAS) module is developed to search for the optimal transformer-based architecture, which further enhances the performance of VI-reID. Additionally, we introduce distillation loss and modality discriminative (MD) loss to examine the potential consistency between different modalities to promote intermodality separation between classes and intramodality compactness within classes. Experimental results on two challenging benchmark datasets illustrate that our developed model achieves state-of-the-art results, outperforming existing VI-reID methods.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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4298.

Zhang, Zhengzhuo; Zhuang, Liansheng

Progressive Neural Architecture Generation with Weaker Predictors Proceedings Article

In: Ide, Ichiro; Kompatsiaris, Ioannis; Xu, Changsheng; Yanai, Keiji; Chu, Wei-Ta; Nitta, Naoko; Riegler, Michael; Yamasaki, Toshihiko (Ed.): MultiMedia Modeling, pp. 229–242, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-2064-7.

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4297.

Su, Yulan; Zhang, Sisi; Lin, Zechao; Wang, Xingbin; Zhao, Lutan; Meng, Dan; Hou, Rui

Poseidon: A NAS-Based Ensemble Defense Method Against Multiple Perturbations Proceedings Article

In: Ide, Ichiro; Kompatsiaris, Ioannis; Xu, Changsheng; Yanai, Keiji; Chu, Wei-Ta; Nitta, Naoko; Riegler, Michael; Yamasaki, Toshihiko (Ed.): MultiMedia Modeling, pp. 215–228, Springer Nature Singapore, Singapore, 2025, ISBN: 978-981-96-2064-7.

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@inproceedings{10.1007/978-981-96-2064-7_16,

title = {Poseidon: A NAS-Based Ensemble Defense Method Against Multiple Perturbations},

author = {Yulan Su and Sisi Zhang and Zechao Lin and Xingbin Wang and Lutan Zhao and Dan Meng and Rui Hou},

editor = {Ichiro Ide and Ioannis Kompatsiaris and Changsheng Xu and Keiji Yanai and Wei-Ta Chu and Naoko Nitta and Michael Riegler and Toshihiko Yamasaki},

isbn = {978-981-96-2064-7},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {MultiMedia Modeling},

pages = {215–228},

publisher = {Springer Nature Singapore},

address = {Singapore},

abstract = {Deep learning models have been proven to be severely affected by adversarial examples, which limit the widespread deployment of deep learning models. Prior research largely focused on defending against single types of perturbations using a single network. However, these methods are susceptible to sacrificing defense range because defense models that are trained to be robust against one perturbation type may not be robust against other types. Moreover, it is unrealistic to assume that neural networks would only be affected by a single type of perturbation. To defend against multiple perturbations, recent works have attempted to improve the overall robustness against multiple perturbations. Nonetheless, when evaluating the model's robustness against each type of perturbation, multi-perturbation defenses are still significantly less effective than models that are robust against a single perturbation type. To address these issues, we propose Poseidon, an ensemble defense method based on neural architecture search to defend against multiple perturbations. We first highlight the importance of architecture evolution in enhancing model robustness. And a novel robust architecture search method is proposed to identify perturbation-tailored architectures for sub-models. Furthermore, we explore a dedicated ensemble method that can combine these diverse sub-model architectures to be robust against multiple types of perturbations. The experimental results demonstrate that Poseidon outperforms the state-of-the-art multiple perturbation defense methods by 10.9% and 7.4% in robustness on the CIFAR-10 and CIFAR-100 datasets, respectively.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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4296.

Amoury, Sofia El; Smili, Youssef; Fakhri, Youssef

Design of an Optimal Convolutional Neural Network Architecture for MRI Brain Tumor Classification by Exploiting Particle Swarm Optimization Journal Article

In: Preprints, 2025.

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4295.

Capello, Alessio; Berta, Riccardo; Ballout, Hadi; Fresta, Matteo; Soltanmuradov, Vafali; Bellotti, Francesco

Enhancing μNAS for 1D CNNs on Microcontrollers Proceedings Article

In: Valle, Maurizio; Gastaldo, Paolo; Limiti, Ernesto (Ed.): Proceedings of SIE 2024, pp. 481–486, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-71518-1.

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4294.

Xu, Jingjing; Wu, Caesar; Li, Yuan-Fang; Danoy, Grégoire; Bouvry, Pascal

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Wang, Mingzi; Meng, Yuan; Tang, Chen; Zhang, Weixiang; Qin, Yijian; Yao, Yang; Li, Yingxin; Feng, Tongtong; Wang, Xin; Guan, Xun; Wang, Zhi; Zhu, Wenwu

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