목록Main (34)

UOMOP

Gaussian Selection Performance (CR : 1/6, 1/24, 1/96) (PS : 2)

import matplotlib.pyplot as plt X = [0, 15, 30] Y_0 = [15.795, 18.989, 19.363] Y_25 = [15.723, 18.881, 19.243] Y_50 = [15.713, 18.634, 18.945] Y_75 = [15.448, 17.907, 18.111] Y_1 = [12.096, 12.095, 12.096] plt.plot(X, Y_0, 'o-', color='blue', label='MR = 0%') plt.plot(X, Y_25, 's--', color='red', label='MR = 25%') plt.plot(X, Y_50, '^-', color='green', label='MR = 50%') plt.plot(X, Y_75, 'x:', c..
Main 2024. 3. 19. 15:18
High Attention Selection Performance (CR : 1/6, 1/24, 1/96) (PS : 2)

import matplotlib.pyplot as plt X = [0, 15, 30] Y_0 = [15.773, 18.986, 19.371] Y_25 = [15.763, 18.903, 19.264] Y_50 = [15.685, 18.683, 19.012] Y_75 = [15.532, 17.897, 18.102] Y_1 = [12.095, 12.096, 12.096] plt.plot(X, Y_0, 'o-', color='blue', label='MR = 0%') plt.plot(X, Y_25, 's--', color='red', label='MR = 25%') plt.plot(X, Y_50, '^-', color='green', label='MR = 50%') plt.plot(X, Y_75, 'x:', c..
Main 2024. 3. 18. 11:32
Low Attention Selection Performance (CR : 1/6, 1/24, 1/96) (PS : 2)

import matplotlib.pyplot as plt X = [0, 15, 30] Y_0 = [15.752, 18.992, 19.364] Y_25 = [15.761, 18.909, 19.272] Y_50 = [15.718, 18.706, 19.015] Y_75 = [15.572, 18.021, 18.232] Y_1 = [12.095, 12.097, 12.099] plt.plot(X, Y_0, 'o-', color='blue', label='MR = 0%') plt.plot(X, Y_25, 's--', color='red', label='MR = 25%') plt.plot(X, Y_50, '^-', color='green', label='MR = 50%') plt.plot(X, Y_75, 'x:', c..
Main 2024. 3. 15. 18:36
Random Selection

import matplotlib.pyplot as plt import torchvision.transforms as transforms import torch.nn.functional as F import math import torch import torchvision import torch.nn as nn import torch.optim as optim import torch.nn.functional as f from torch.utils.data import DataLoader, Dataset import time from params import * import os from tqdm import tqdm device = torch.device("cuda:0" if torch.cuda.is_av..
Main 2024. 3. 15. 18:31
High Attention Selection

import matplotlib.pyplot as plt import torchvision.transforms as transforms import torch.nn.functional as F import math import torch import torchvision import torch.nn as nn import torch.optim as optim import torch.nn.functional as f from torch.utils.data import DataLoader, Dataset import time from params import * import os from tqdm import tqdm device = torch.device("cuda:0" if torch.cuda.is_av..
Main 2024. 3. 15. 18:29
Low Attention Selection

import matplotlib.pyplot as plt import torchvision.transforms as transforms import torch.nn.functional as F import math import torch import torchvision import torch.nn as nn import torch.optim as optim import torch.nn.functional as f from torch.utils.data import DataLoader, Dataset import time from params import * import os from tqdm import tqdm device = torch.device("cuda:0" if torch.cuda.is_av..
Main 2024. 3. 15. 18:28
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