2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDSS-NNC.2
Paper Title Optimization-based Neural Networks Compression
Authors Younes Tahiri, CEA List, France; Mohamed El Amine Seddik, Ecole Polytechnique, France; Mohamed Tamaazousti, CEA List, France
SessionSS-NNC: Special Session: Neural Network Compression and Compact Deep Features
LocationArea B
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Special Sessions: Neural Network Compression and Compact Deep Features: From Methods to Standards
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents a method for constructing a \textit{size compressed} neural network with better or similar accuracy than a given dense neural network, therefore the compressed network requires less memory and computational resources. The presented method relies basically on learning successive mappings between the given dense neural network (teacher) hidden features and the size-compressed neural network (student) hidden features, where the latter is learned also to solve the initial task of the teacher network. The presented method is particularly compared to baselines where we specifically show that the additional learned mappings significantly improve the performance (accuracy and computation) of the student network.