부분적인 스크린 영상 혼합을 통한 합성곱 신경망의 영상 인식 성능 향상

다국어 초록 (Multilingual Abstract)

Data augmentation is a way of improving the generalization ability of deep neural networks by expanding their training data with pre-defined transformations. For image recognition tasks, traditional data augmentation methods transform an image by using simple techniques such as random horizontal flipping and cropping. However, several recent methods have been proposed to augment training data by linearly interpolating two images with arbitrary proportions. Although they can further improve the generalization ability, they use a randomly chosen mixing ratio throughout a pair of images, which may ignore their local characteristics. In this paper, we propose a novel data augmentation method that can vary the mixing ratio according to the local brightness. Our method partially blends two images with Screen blend mode. We have also shown that CNNs can be successfully trained, only with the blended inputs. Experimental results on the CIFAR-10 and CIFAR-100 datasets have shown that the proposed method yields superior performance than existing methods.

목차 (Table of Contents)

• Abstract
• I. 서론
• II. 본론
• Ⅲ. 실험 및 결과
• Ⅳ. 결론 및 향후 연구 방향
• 참고문헌