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 In representation learning domain, the mainstream methods for model ensemble include “implicit” ensemble approaches, such as using techniques like dropout, and “explicit” ensemble methods, such as voting or weighted averaging based on multiple model outputs. Compared to implicit ensemble techniques, explicit ensemble methods allow for more flexibility in combining models with different structures to obtain different perspectives on representations. However, the representations obtained from different models do not guarantee a linear relationship, and simply linearly combining multiple model outputs may result in a degraded performance. Meanwhile, employing non‐linear fusion mechanisms such as distillation and meta‐learning can be uninterpretable and time‐consuming. To this end, we propose the hypothesis of linear fusion based on the output representations of deep learning models, and design a interpretable linear fusion method based on this hypothesis. This method applies a transform layer to map the output representations of different models to the same classification center. Experimental results demonstrate that compared to directly averaging the representations, our method achieves better performance. Additionally, our method retains the convenience of direct averaging while offering improved performance in terms of time and computational efficiency compared to non‐linear fusion. Furthermore, we test the applicability of our method in both computer vision and natural language processing representation tasks using supervised and semi‐supervised approaches.
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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