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.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2023.11
- 수록면
- 608 - 612 (5page)
이용수
초록· 키워드
오류제보하기
Although the significant advances in image super-resolution (SR) with convolutional neural networks (CNNs) have led to continuous performance improvement, the state-of-the-art (SOTA) SR models face the challenges conducting in resource-constrained environments due to its complexity. Quantization, one of the neural network optimizations, is used to address these challenges. However, Quantization for the SR model should take its characteristics into 1account. To address this issue, we propose Frequency Aware Multi-Bit Quantization (FAMBQ), which considers the frequency in input image and layers to select adequate bit-width to quantize each layer. Frequency is the basic component in image that helps improve the SR performance, proved in numerous recent works[1-2]. We apply our quantization approach across various widely used super-resolution architectures, including EDSR[3], IMDN[4]. Experiments show that our framework achieve comparable PSNR to full precision original model and SOTA frameworks for low bit-width quantization on four benchmark datasets.
목차
Abstract
Ⅰ. 서론
Ⅱ. 본론
Ⅲ. 실험 결과
Ⅳ. 결론 및 향후 연구 방향
참고문헌
참고문헌 (0)
참고문헌 신청이 논문의 저자 정보
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0