Discrepancies among pre-trained deep neural networks: a new threat to model zoo reliability

Author:

Montes Diego1,Peerapatanapokin Pongpatapee1,Schultz Jeff1,Guo Chengjun1,Jiang Wenxin1,Davis James C.1

Affiliation:

1. Purdue University, USA

Funder

Cisco Systems

Google

NSF (National Science Foundation)

Publisher

ACM

Reference41 articles.

1. 2019. ONNX | Home. https://onnx.ai/ 2019. ONNX | Home. https://onnx.ai/

2. Software Engineering for Machine Learning: A Case Study

3. Vishnu Banna , Akhil Chinnakotla , Zhengxin Yan , Anirudh Vegesana , Naveen Vivek , Kruthi Krishnappa , Wenxin Jiang , Yung-Hsiang Lu , George K. Thiruvathukal , and James C . Davis . 2021 . An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors . https://doi.org/10.48550/arXiv.2107.00821 10.48550/arXiv.2107.00821 Vishnu Banna, Akhil Chinnakotla, Zhengxin Yan, Anirudh Vegesana, Naveen Vivek, Kruthi Krishnappa, Wenxin Jiang, Yung-Hsiang Lu, George K. Thiruvathukal, and James C. Davis. 2021. An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors. https://doi.org/10.48550/arXiv.2107.00821

4. blacklong28. 2022. onnx-opcounter. https://github.com/blacklong28/onnxopcounter blacklong28. 2022. onnx-opcounter. https://github.com/blacklong28/onnxopcounter

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