ModelHub.AI: Dissemination Platform for Deep Learning Models

Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others. Additionally, the availability of open-source computational frameworks has lowered the barriers to implementing state-of-the-art methods across multiple domains. Albeit leading to major performance breakthroughs in some tasks, effective dissemination of deep learning algorithms remains challenging, inhibiting reproducibility and benchmarking studies, impeding further validation, and ultimately hindering their effectiveness in the cumulative scientific progress. In developing a platform for sharing research outputs, we present this http URL (this http URL), a community-driven container-based software engine and platform for the structured dissemination of deep learning models. For contributors, the engine controls data flow throughout the inference cycle, while the contributor-facing standard template exposes model-specific functions including inference, as well as pre- and post-processing. Python and RESTful Application programming interfaces (APIs) enable users to interact with models hosted on this http URL and allows both researchers and developers to utilize models out-of-the-box. this http URL is domain-, data-, and framework-agnostic, catering to different workflows and contributors' preferences.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  MyungJae Shin,et al.  Top-down parsing for Neural Network Exchange Format (NNEF) in TensorFlow-based deep learning computation , 2018, 2018 International Conference on Information Networking (ICOIN).

[3]  Phi Vu Tran,et al.  A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI , 2016, ArXiv.

[4]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[5]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[6]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[7]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Senthil Mani,et al.  DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers , 2017, AAAI.

[9]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Ahmed Hosny,et al.  Artificial intelligence in radiology , 2018, Nature Reviews Cancer.

[11]  Rajesh Chandrakar.  Digital object identifier system: an overview , 2006, Electron. Libr..

[12]  Luis Ibáñez,et al.  The Design of SimpleITK , 2013, Front. Neuroinform..

[13]  Pearl Brereton,et al.  Reproducibility in Machine Learning-Based Studies: An Example of Text Mining , 2017 .

[14]  Ian T. Foster,et al.  DLHub: Model and Data Serving for Science , 2018, 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS).

[15]  R. Gillies,et al.  Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study , 2018, PLoS medicine.

[16]  Christos Davatzikos,et al.  Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features , 2017, Scientific Data.

[17]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[18]  et al.,et al.  Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge , 2018, ArXiv.

[19]  Christopher Olston,et al.  TensorFlow-Serving: Flexible, High-Performance ML Serving , 2017, ArXiv.

[20]  Zheng Zhang,et al.  MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems , 2015, ArXiv.

[21]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[23]  Franz J. Király,et al.  NIPS - Not Even Wrong? A Systematic Review of Empirically Complete Demonstrations of Algorithmic Effectiveness in the Machine Learning and Artificial Intelligence Literature , 2018, ArXiv.

[24]  Yasin Almalioglu,et al.  Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[25]  M. Hutson.  Artificial intelligence faces reproducibility crisis. , 2018, Science.

[26]  Christos Davatzikos,et al.  Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome , 2018, Journal of medical imaging.

[27]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[28]  Xin Wang,et al.  Clipper: A Low-Latency Online Prediction Serving System , 2016, NSDI.

[29]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[30]  Jun Cheng,et al.  Kipoi: accelerating the community exchange and reuse of predictive models for genomics , 2018, bioRxiv.

[31]  Seyed-Ahmad Ahmadi,et al.  Cloud Deployment of High-Resolution Medical Image Analysis With TOMAAT , 2019, IEEE Journal of Biomedical and Health Informatics.

[32]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[33]  Larry S. Davis,et al.  Towards Unified Data and Lifecycle Management for Deep Learning , 2016, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).

[34]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[35]  Vanessa Sochat,et al.  Singularity: Scientific containers for mobility of compute , 2017, PloS one.

[36]  Margot Mieskes,et al.  A Quantitative Study of Data in the NLP community , 2017, EthNLP@EACL.

[37]  Emad Barsoum,et al.  Training deep networks for facial expression recognition with crowd-sourced label distribution , 2016, ICMI.

[38]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[39]  Dorit Merhof,et al.  Segmentation of Brain Tumors and Patient Survival Prediction: Methods for the BraTS 2018 Challenge , 2018, BrainLes@MICCAI.

[40]  François Chollet,et al.  Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Odd Erik Gundersen,et al.  State of the Art: Reproducibility in Artificial Intelligence , 2018, AAAI.

[42]  Purang Abolmaesumi,et al.  DeepInfer: open-source deep learning deployment toolkit for image-guided therapy , 2017, Medical Imaging.

[43]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[44]  Stefanos Zafeiriou,et al.  ArcFace: Additive Angular Margin Loss for Deep Face Recognition , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  Garrison W. Cottrell,et al.  Understanding Convolution for Semantic Segmentation , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[46]  Richard McKinley,et al.  Ensembles of Densely-Connected CNNs with Label-Uncertainty for Brain Tumor Segmentation , 2018, BrainLes@MICCAI.

[47]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[48]  Seyed-Ahmad Ahmadi,et al.  Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields , 2016, MICCAI.

[49]  Nikolaos Doulamis,et al.  Deep Learning for Computer Vision: A Brief Review , 2018, Comput. Intell. Neurosci..

[50]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).