17.5. WGAN （Wasserstein Generative Adversarial Networks）

[Arjovsky et al., 2017]

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from typing import Any, Union, Callable, Type, TypeVar
from tqdm.std import trange,tqdm
import numpy as np 
import numpy.typing as npt
import pandas as pd 
import matplotlib.pyplot as plt 
import plotly.express as px
import seaborn as sns
from PIL import Image
import cv2
import requests

# pytorch関連のimport
import torch
import torch.nn as nn 
import torch.nn.functional as F 
import torch.optim as optim 
import torchvision
import torchvision.transforms as transforms

from src import utils
SEED = 2023_7_17
utils.set_seed(SEED)

17.5.1. WGANの仕組み

17.5.2. 実験

17.5.2.1. 実験結果

17.6. 参考文献

17.6.1. 論文等

ACB17

Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein generative adversarial networks. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, 214–223. PMLR, 2017.