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

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Paper Detail

Paper IDMLR-APPL-IP-5.7
Paper Title IMPROVING FILLING LEVEL CLASSIFICATION WITH ADVERSARIAL TRAINING
Authors Apostolos Modas, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Alessio Xompero, Ricardo Sánchez-Matilla, Queen Mary University of London, United Kingdom; Pascal Frossard, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Andrea Cavallaro, Queen Mary University of London, United Kingdom
SessionMLR-APPL-IP-5: Machine learning for image processing 5
LocationArea E
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We investigate the problem of classifying - from a single image - the level of content in a cup or a drinking glass. This problem is made challenging by several ambiguities caused by transparencies, shape variations and partial occlusions, and by the availability of only small training datasets. In this paper, we tackle this problem with an appropriate strategy for transfer learning. Specifically, we use adversarial training in a generic source dataset and then refine the training with a task-specific dataset. We also discuss and experimentally evaluate several training strategies and their combination on a range of container types of the CORSMAL Containers Manipulation dataset. We show that transfer learning with adversarial training in the source domain consistently improves the classification accuracy on the test set and limits the overfitting of the classifier to specific features of the training data.