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As mentioned in the README, the code for DP-NAS is derived from our previous CODEBRIM CVPR 2019 work, entitled "Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset; Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos and Visvanathan Ramesh; Conference on Computer Vision and Pattern Recognition, 2019".

As such, the non-commercial original licensing agreement is extended for this work.

DP-NAS license statement

The researcher has requested permission to use the deep prior architecture search (DP-NAS) code provided by the authors at Goethe University. In exchange for such permission the researcher agrees and is bound by the following terms and conditions:

  1. The DP-NAS code comes "AS IS". While we have made every effort to ensure accuracy, no representations or warranties regarding the code is made. This includes but is not limited to warranties of non-infringement or fitness for a particular purpose and no responsibility is accepted for errors or omissions.
  2. The researcher shall use the code for non-commercial research and educational purposes only. If the researcher is employed by a for-profit, commercial entitity, the researcher's employer shall also be bound by these terms and conditions and the researcher hereby represents full authorization to enter this agreement on behalf of such employer.
  3. The researcher may not use modified versions code or any derivative works (such as additional trained models) to procure a commercial gain.
  4. Access to the code to fellow research associates and colleagues may be granted provided that they first agree to be bound by these terms and conditions.
  5. Derivative works such as additional trained models or extended code are permissible to be shared under the same license agreement for non-commercial and educational purposes.
  6. The researcher shall further include a reference to the corresponding publication: "Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference; Martin Mundt, Iuliia Pliushch, and Visvanathan Ramesh; Conference on Computer Vision and Pattern Recognition, Workshop on Continual Learning (CLVision), 2021" in any work that makes use of the code.
  7. All rights not expressly granted to the researcher are reserved by us. They reserve the right to terminate the researcher's access to the database at any time.