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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
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This work proposes a small DNN architecture called SqueezeNet, which achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters and is able to compress to less than 0.5MB (510x smaller than AlexNet).Abstract:
Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training. (2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet).
The SqueezeNet architecture is available for download here: this https URLread more
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Deep Learning in Skin Disease Image Recognition: A Review
TL;DR: The results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.
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COMIC: Toward A Compact Image Captioning Model With Attention
TL;DR: This paper showed that the proposed model, named COMIC for compact image captioning, achieves comparable results in five common evaluation metrics with state-of-the-art approaches on both MS-COCO and InstaPIC-1.1M datasets despite having an embedded vocabulary size that is 39×−99× smaller.
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AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study
Paolo Soda,Natascha Claudia D'Amico,Jacopo Tessadori,Giovanni Valbusa,Valerio Guarrasi,Chandra Bortolotto,Muhammad Usman Akbar,Rosa Sicilia,Ermanno Cordelli,Deborah Fazzini,Michaela Cellina,Giancarlo Oliva,Giovanni Callea,Silvia Panella,Maurizio Cariati,Diletta Cozzi,Vittorio Miele,Elvira Stellato,Gianpaolo Carrafiello,Giulia Castorani,A. Simeone,Lorenzo Preda,Giulio Iannello,Alessio Del Bue,Fabio Tedoldi,Marco Alì,Diego Sona,Sergio Papa +27 more
TL;DR: This work investigates whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death, and investigates the potential of artificial intelligence to predict the prognosis of such patients.
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Extreme Network Compression via Filter Group Approximation
TL;DR: A novel decomposition method based on filter group approximation, which can significantly reduce the redundancy of deep convolutional neural networks (CNNs) while maintaining the majority of feature representation, is proposed.
Journal ArticleDOI
ADID-UNET—a segmentation model for COVID-19 infection from lung CT scans
Alex Noel Joseph Raj,Haipeng Zhu,Asiya Khan,Zhemin Zhuang,Zengbiao Yang,Vijayalakshmi G. V. Mahesh,Ganesan Karthik +6 more
TL;DR: The experimental results show that the ADID-UNET model can accurately segment COVID-19 lung infected areas, with performance measures greater than 80% for metrics like Accuracy, Specificity and Dice Coefficient, and the proposed model showed excellent segmentation effects.
References
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Proceedings ArticleDOI
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