그림 1. 컨볼루셔널 뉴럴 네트워크의 아키텍처 예시 (이미지 분류) Fig. 1. Example of convolutional neural networks (Image classification)
그림 2. VGG11 아키텍처를 기반으로 한 U-net 아키텍처 (시멘틱 세그멘테이션)[8] Fig. 2. U-net architecture based on VGG11 model (Semantic segmentation)[8]
그림 3. 다운샘플링 과정에서 패션 아이템의 특징을 잃은 사례 (FCN 기반 아키텍처) Fig. 3. Case that lost feature of fashion item in downsampling process (FCN based model)
그림 4. 밀집 블록(Dense block)에서의 밀집 연결성 개념 Fig. 4. Dense connectivity concept of dense block
그림 5. 데이터 세트 예시 Fig. 5. Sample of data set
그림 6. 시멘틱 세그멘테이션 결과 예시 Fig. 6. Examples of semantic segmentation
그림 7. FCN 기반 아키텍처와의 결과 비교 Fig. 7. Comparison of results with FCN-based architecture
그림 8. 배경과 인물의 영역 분류에는 성공하였으나, 오브젝트의 디테일을 추론하지 못한 사례 Fig. 8. Successful classification of the background and person, but the case of not inferring the detail of the object
표 1. Fully convolutional densenet layers Table 1. Fully convolutional densenet layers
표 2. 데이터 세트 명세 Table 2. Data Set Description
표 3. 데이터 세트별 학습 결과 Table 3. Learning results by data set
표 4. 학습 관련 파라미터 Table 4. Learning parameters
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