Smart Media Journal (스마트미디어저널)

THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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Superpixel Exclusion-Inclusion Multiscale Approach for Explanations of Deep Learning

딥러닝 설명을 위한 슈퍼픽셀 제외·포함 다중스케일 접근법

  • 서다솜 (전북대학교 컴퓨터공학부) ;
  • 오강한 (전북대학교 컴퓨터공학부) ;
  • 오일석 (전북대학교 컴퓨터공학부) ;
  • 유태웅 (전북대학교 컴퓨터공학부)
  • Received : 2019.05.29
  • Accepted : 2019.06.13
  • Published : 2019.06.30
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Abstract

As deep learning has become popular, researches which can help explaining the prediction results also become important. Superpixel based multi-scale combining technique, which provides the advantage of visual pleasing by maintaining the shape of the object, has been recently proposed. Based on the principle of prediction difference, this technique computes the saliency map from the difference between the predicted result excluding the superpixel and the original predicted result. In this paper, we propose a new technique of both excluding and including super pixels. Experimental results show 3.3% improvement in IoU evaluation.

딥러닝이 보편화되면서 예측 결과를 설명하는 연구가 중요해졌다. 최근 슈퍼픽셀에 기반한 다중스케일 결합 기법이 제안되었는데, 물체의 모양을 유지함으로써 시각적 공감이라는 장점을 제공한다. 이 기법은 예측 차이라는 원리에 기반을 두고 있으며, 슈퍼픽셀을 가리고 얻은 예측 결과와 원래 예측 결과의 차이를 보고 돌출맵을 구성한다. 본 논문은 슈퍼픽셀을 가리는 제외 연산뿐 아니라 슈퍼픽셀만 보여주는 포함 연산까지 사용하는 새로운 기법을 제안한다. 실험 결과 제안한 방법은 IoU에서 3.3%의 성능 향상을 보인다.

Keywords

References

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