인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract With the demands for light deep networks models in various front-end devices, network compression has attracted increasing interest for reducing model sizes yet without sacrificing much model accuracy. This paper presents a multifarious knowledge transfer network (MKTN) that aims to produce a compact yet powerful student network from two complementary teacher networks. Instead of learning homogeneous features, the idea is to pre-train one teacher to capture generative and low-level image features under a reconstruction objective, and another teacher to capture discriminative and task-specific features under the same objective as the student network. During knowledge transfer, the student learns multifarious and complementary knowledge from the two teacher networks under the guidance of the proposed adversarial loss and feature loss respectively. Experimental results indicate that the proposed training losses can effectively guide the student to learn spatial-level and pixel-level information as distilled from teacher networks. On the other hand, our study over a number of widely used datasets shows that transferring multifarious features from complementary teachers equipped with different types of knowledge helps to teach a compact yet powerful student effectively.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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