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-6.3
Paper Title KNOWLEDGE TRANSFERRED FINE-TUNING FOR ANTI-ALIASED CONVOLUTIONAL NEURAL NETWORK IN DATA-LIMITED SITUATION
Authors Satoshi Suzuki, Shoichiro Takeda, Ryuichi Tanida, Hideaki Kimata, NTT Corporation, Japan; Hayaru Shouno, University of Electro-Communications, Japan
SessionMLR-APPL-IP-6: Machine learning for image processing 6
LocationArea E
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Anti-aliased convolutional neural networks~(CNNs) introduce blur filters to intermediate representations in CNNs to achieve high accuracy. A promising way to build a new anti-aliased CNN is to fine-tune a pre-trained CNN, which can easily be found online, with blur filters. However, blur filters drastically degrade the pre-trained representation, so the fine-tuning needs to rebuild the representation by using massive training data. Therefore, if the training data is limited, the fine-tuning cannot work well because it induces overfitting to the limited training data. To tackle this problem, this paper proposes ``knowledge transferred fine-tuning.'' On the basis of the idea of knowledge transfer, our method transfers the knowledge from intermediate representations in the pre-trained CNN to the anti-aliased CNN while fine-tuning. We transfer only essential knowledge using a pixel-level loss that transfers detailed knowledge and a global-level loss that transfers coarse knowledge. Experimental results demonstrate that our method significantly outperforms the simple fine-tuning method.