davidstutz / iccv2021-robust-flatness

ICCV'21 paper "Relating Adversarially Robust Generalization to Flat Minima".

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

Relating Adversarially Robust Generalization to Flat Minima

This repository contains data and code corresponding to

D. Stutz, M.Hein, B.Schiele. Relating Adversarially Robust Generalization to Flat Minima. ICCV, 2021

Please cite as:

@article{Stutz2021ICCV,
    author    = {David Stutz and Matthias Hein and Bernt Schiele},
    title     = {Relating Adversarially Robust Generalization to Flat Minima},
    booktitle = {IEEE International Conference on Computer Vision (ICCV)},
    publisher = {IEEE Computer Society},
    year      = {2021}
}

Also check the project page for the final publication, code and data.

Relating Adversarially Robust Generalization to Flat Minima.

License

Licenses for source code and data corresponding to:

D. Stutz, M.Hein, B.Schiele. Relating Adversarially Robust Generalization to Flat Minima. ICCV, 2021

Note that the source code and/or data is based on other projects for which separate licenses apply. See the corresponding subdirectory for details.

Copyright (c) 2021 David Stutz, Max-Planck-Gesellschaft

Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use this software and associated documentation files (the "Software").

The authors hereby grant you a non-exclusive, non-transferable, free of charge right to copy, modify, merge, publish, distribute, and sublicense the Software for the sole purpose of performing non-commercial scientific research, non-commercial education, or non-commercial artistic projects.

Any other use, in particular any use for commercial purposes, is prohibited. This includes, without limitation, incorporation in a commercial product, use in a commercial service, or production of other artefacts for commercial purposes.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

You understand and agree that the authors are under no obligation to provide either maintenance services, update services, notices of latent defects, or corrections of defects with regard to the Software. The authors nevertheless reserve the right to update, modify, or discontinue the Software at any time.

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. You agree to cite the corresponding papers (see above) in documents and papers that report on research using the Software.

About

ICCV'21 paper "Relating Adversarially Robust Generalization to Flat Minima".


Languages

Language:PostScript 50.7%Language:TeX 49.3%