목록분류 전체보기 (295)
UOMOP
% 데이터masking_ratio = 0.1:0.1:0.9;topk = [0.029, 0.042, 0.053, 0.06, 0.065, 0.067, 0.069, 0.072, 0.074]; % 예시 데이터random = [0.028, 0.032, 0.036, 0.04, 0.044, 0.0455, 0.046, 0.046, 0.046]; % 예시 데이터proposed = [0.027, 0.042, 0.055, 0.061, 0.064, 0.066, 0.066, 0.067, 0.069]; % 예시 데이터% 그림 그리기figure;hold on;plot(masking_ratio, proposed, ':r*', 'LineWidth', 2.5, 'MarkerSize', 12, 'DisplayName', 'Proposed..
% 데이터masking_ratio = 0.1:0.1:0.9;topk = [8, 15, 23, 37, 57, 80, 104, 133, 172]; % 예시 데이터random = [0, 0, 8, 20, 39, 63, 98, 136, 180]; % 예시 데이터proposed = [0, 0, 0, 0, 0, 20, 53, 93, 152]; % 예시 데이터% 그림 그리기figure;hold on;plot(masking_ratio, proposed, ':r*', 'LineWidth', 2.5, 'MarkerSize', 12, 'DisplayName', 'Proposed'); % CBS 빨간 점선plot(masking_ratio, topk, '--bs', 'LineWidth', 2.5, 'MarkerSize', 8,..
% 기존 데이터 입력 (FLOPs 데이터)snr_512 = [30, 20, 10, 0];flops_proposed_512 = [20855.182, 20073.754, 18959.42, 17114.83];flops_TopK_512 = [37627.156, 32831.124, 25740.668, 21987.248];flops_Random_512 = [42444.78, 36116.336, 25495.256, 19640.542];snr_256 = [30, 20, 10, 0];flops_proposed_256 = [26347.008, 23197.228, 19014.698, 17960.544];flops_TopK_256 = [36023.62, 31121.048, 23387.992, 21973.838];flops_R..
% 기존 데이터 입력snr_512 = [30, 20, 10, 0];psnr_proposed_512 = [30.99, 28.68, 24.98, 21.05];psnr_TopK_512 = [29.99, 27.82, 24.19, 20.41];psnr_Random_512 = [30.00, 27.38, 23.24, 19.02];snr_256 = [30, 20, 10, 0];psnr_proposed_256 = [27.35, 25.98, 23.23, 20.20];psnr_TopK_256 = [26.80, 25.34, 22.61, 19.32];psnr_Random_256 = [26.92, 25.24, 21.69, 17.96];% DeepJSCC 데이터 추가psnr_DeepJSCC_256 = [19.638, 23.318,..
CBS (alpha: 2.1, beta: 0.6)dim:512, snr:30, alpha:2.1, beta:0.6 Lv1:2321, Lv2:321, Lv3:230, Lv4:7128 final FLOPs : 20855.182 Average PSNR over 10000 images: 29.99 Data loaded from files. dim:512, snr:20, alpha:2.1, beta:0.6 Lv1:1627, Lv2:995, Lv3:680, Lv4:6698 final FLOPs : 20073.754 Average PSNR over 10000 images: 27.68 Data loaded from files. dim:512, snr:10, alpha:2.1, beta:0.6 Lv1:476, Lv2:7..
% alpha와 beta의 값들alpha_values = [0.1, 0.4, 0.7, 1.0, 1.3, 1.6];beta_values = [0.1, 0.4, 0.7, 1.0, 1.3, 1.6];% 데이터: alpha, beta, dim, snr, Average PSNR, FLOPsdata = [ 0.1, 0.1, 512, 30, 30.17, 42615.213; 0.1, 0.4, 512, 30, 30.13, 41735.868; 0.1, 0.7, 512, 30, 30.04, 40109.326; 0.1, 1.0, 512, 30, 29.89, 37927.21; 0.1, 1.3, 512, 30, 29.69, 35437.803; 0.1, 1.6, 512, 30, 29.46, 3313..