Multiobjective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification

Lu, Zhichao; Whalen, Ian; Dhebar, Yashesh; Deb, Kalyanmoy; Goodman, Erik D.; Banzhaf, Wolfgang; Boddeti, Vishnu Naresh

doi:10.1109/tevc.2020.3024708

Cited by 140 publications

(88 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rather than generating the entire CNNs, the micro-search space [46] has also been successfully employed by many recent EA-based NAS algorithms [83][84][85][86][87]. Real et al [85] propose an extension of the large-scale evolution [73], called AmoebaNet, which has achieved better results on ImageNet compared with hand-designed methods for the first time.…”

Section: Nas Based On Easmentioning

confidence: 99%

“…To reduce the computational burden for fitness evaluations, another widely adopted approaches are to train and evaluate individuals using proxy metrics [85][86][87]. The performance of the proxy models is used as the surrogate measurements to guide the evolutionary search.…”

Section: Nas Based On Easmentioning

confidence: 99%

“…Hence, the EcoNAS method is able to achieve a 400× reduced search time in comparison to AmoebaNet [85] without sacrificing the performance. Lu et al [87] empirically establish the trade-off between the correlation of proxy performance to true performance and the speed-up in estimation.…”

Section: Nas Based On Easmentioning

confidence: 99%

“…Moreover, a Bayesian network (BN) is adopted to learn the knowledge about promising architectures present in the search history and then guide the future exploitation in generating new architectures. Subsequently, Lu et al suggest NSGANet-v2 [87], an extension of NSGANet [84], where a comprehensive search space including more layer operations and one more option that controls the width of the model is introduced. Dong et al [98] present a DPP-Net on the basis of [99] that optimizes both GPU memory usage and the model performance.…”

Section: Nas Based On Easmentioning

confidence: 99%

See 3 more Smart Citations

From federated learning to federated neural architecture search: a survey

Zhu

Zhang

Jin

2021

Complex Intell. Syst.

115

View full text Add to dashboard Cite

Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern. Meanwhile, neural architecture search has become very popular in deep learning for automatically tuning the architecture and hyperparameters of deep neural networks. While both federated learning and neural architecture search are faced with many open challenges, searching for optimized neural architectures in the federated learning framework is particularly demanding. This survey paper starts with a brief introduction to federated learning, including both horizontal, vertical, and hybrid federated learning. Then neural architecture search approaches based on reinforcement learning, evolutionary algorithms and gradient-based are presented. This is followed by a description of federated neural architecture search that has recently been proposed, which is categorized into online and offline implementations, and single- and multi-objective search approaches. Finally, remaining open research questions are outlined and promising research topics are suggested.

show abstract

Section: Nas Based On Easmentioning

confidence: 99%

Section: Nas Based On Easmentioning

confidence: 99%

Section: Nas Based On Easmentioning

confidence: 99%

Section: Nas Based On Easmentioning

confidence: 99%

See 2 more Smart Citations

From federated learning to federated neural architecture search: a survey

Zhu

Zhang

Jin

2021

Complex Intell. Syst.

115

View full text Add to dashboard Cite

show abstract

“…In some studies, training performance is further improved by some additional operations. For example, (Lu et al, 2019b) appends an auxiliary head classifier to the architecture, but we did not follow these approaches that require manual intervention after the search process.…”

Section: Training After the Search Processmentioning

confidence: 99%

Table 4: Evaluation of the final fronts with respect to three metrics and the number of solutions.

View full text Add to dashboard Cite

Advancement of deep learning in pneumonia/Covid‐19 classification and localization: A systematic review with qualitative and quantitative analysis

Shah

2022

Chronic Diseases and Translational Medicine

View full text Add to dashboard Cite

Around 450 million people are affected by pneumonia every year, which results in 2.5 million deaths. Coronavirus disease 2019 (Covid‐19) has also affected 181 million people, which led to 3.92 million casualties. The chances of death in both of these diseases can be significantly reduced if they are diagnosed early. However, the current methods of diagnosing pneumonia (complaints + chest X‐ray) and Covid‐19 (real‐time polymerase chain reaction) require the presence of expert radiologists and time, respectively. With the help of deep learning models, pneumonia and Covid‐19 can be detected instantly from chest X‐rays or computerized tomography (CT) scans. The process of diagnosing pneumonia/Covid‐19 can become faster and more widespread. In this paper, we aimed to elicit, explain, and evaluate qualitatively and quantitatively all advancements in deep learning methods aimed at detecting community‐acquired pneumonia, viral pneumonia, and Covid‐19 from images of chest X‐rays and CT scans. Being a systematic review, the focus of this paper lies in explaining various deep learning model architectures, which have either been modified or created from scratch for the task at hand. For each model, this paper answers the question of why the model is designed the way it is, the challenges that a particular model overcomes, and the tradeoffs that come with modifying a model to the required specifications. A grouped quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal. Some tradeoffs cannot be quantified and, hence, they are mentioned explicitly in the qualitative analysis, which is done throughout the paper. By compiling and analyzing a large quantum of research details in one place with all the data sets, model architectures, and results, we aimed to provide a one‐stop solution to beginners and current researchers interested in this field.

show abstract

Multiobjective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification

Cited by 140 publications

References 33 publications

From federated learning to federated neural architecture search: a survey

From federated learning to federated neural architecture search: a survey

Table 4: Evaluation of the final fronts with respect to three metrics and the number of solutions.

Advancement of deep learning in pneumonia/Covid‐19 classification and localization: A systematic review with qualitative and quantitative analysis

Contact Info

Product

Resources

About