Dabarun Zurfin Koyo Na Ci Gaba Don Sarrafa Hotuna Da Bincike

1. Gabatarwa

Zurfin koyo ya kawo sauyi ga sarrafa hotuna da hangen nesa na kwamfuta, yana ba da damar samarwa, haɓakawa, da binciken hotuna ba a taɓa yin irinsa ba. Wannan takarda tana bincika hanyoyin ci gaba a cikin sarrafa hotuna na tushen zurfin koyo, tana mai da hankali kan tushen ka'idoji da kuma aiwatarwa na aiki.

2. Tushen Zurfin Koyo

3. Cibiyoyin Sadarwar Jijiya Masu Hamayya

4. Tushen Lissafi

Muhimman ka'idojin lissafi sun haɗa da ka'idar ingantawa, rarraba yuwuwar, da ka'idar bayanai. Bambancin Kullback-Leibler yana auna bambanci tsakanin rarraba bayanai da aka samar da na gaske:

$D_{KL}(P || Q) = \sum_{x} P(x) \log \frac{P(x)}{Q(x)}$

Ingantattun dabarun ingantawa kamar Adam da RMSprop suna tabbatar da ingantaccen haɗuwa yayin horo.

5. Sakamakon Gwaje-gwaje

Cikakkun gwaje-gwaje sun nuna tasirin hanyoyin zurfin koyo a cikin ayyukan sarrafa hotuna. Ma'aunin kimantawa sun haɗa da Ma'aunin Ƙarar Siginar Kololuwa (PSNR), Ma'aunin Kamancen Tsari (SSIM), da Nisan Farko na Farko (FID).

Hoto na 1 yana kwatanta kwatancin ingancin sakamakon ƙwararrun hotuna, yana nuna gagarumin ci gaba a cikin ingancin gani da kiyaye cikakkun bayanai idan aka kwatanta da hanyoyin gargajiya.

6. Aiwatar da Lamba

Lambar Python mai zuwa tana nuna ainihin aiwatar da GAN ta amfani da PyTorch:

import torch
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self, latent_dim, img_channels):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            nn.ConvTranspose2d(latent_dim, 512, 4, 1, 0, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(True),
            nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(True),
            nn.ConvTranspose2d(256, img_channels, 4, 2, 1, bias=False),
            nn.Tanh()
        )
    
    def forward(self, input):
        return self.main(input)

# Misalin madauki na horo
for epoch in range(num_epochs):
    for i, (real_imgs, _) in enumerate(dataloader):
        # Horar da mai nuna bambanci
        optimizer_D.zero_grad()
        z = torch.randn(batch_size, latent_dim, 1, 1)
        fake_imgs = generator(z)
        real_loss = adversarial_loss(discriminator(real_imgs), real_labels)
        fake_loss = adversarial_loss(discriminator(fake_imgs.detach()), fake_labels)
        d_loss = (real_loss + fake_loss) / 2
        d_loss.backward()
        optimizer_D.step()
        
        # Horar da mai samarwa
        optimizer_G.zero_grad()
        g_loss = adversarial_loss(discriminator(fake_imgs), real_labels)
        g_loss.backward()
        optimizer_G.step()
        

7. Aikace-aikace Na Gaba

Sabbin aikace-aikace na zurfin koyo a cikin sarrafa hotuna sun haɗa da:

• Hoton Magani: Bincike ta atomatik da tsarin magani
• Motocin Cin Gashin Kai: Ingantaccen fahimta da fahimtar yanayi
• Hoton Tauraron Dan Adam: Sa ido kan muhalli da tsarin birane
• Masana'antu Na Ƙirƙira: Ƙirƙirar fasaha da abun ciki na taimakon AI
• Tsarin Tsaro: Ci gaban sa ido da gano barazana

Hanyoyin bincike na gaba suna mai da hankali kan inganta fahimtar ƙira, rage buƙatun lissafi, da haɓaka haɓaka a fagage daban-daban.

8. Nassoshi

1. Goodfellow, I., et al. "Cibiyoyin Sadarwar Jijiya Masu Hamayya." Ci gaba a cikin Tsarin Sarrafa Bayanai na Jijiya, 2014.
2. He, K., et al. "Koyon Saura Mai Zurfi don Gane Hotuna." CVPR, 2016.
3. Ronneberger, O., et al. "U-Net: Cibiyoyin Sadarwa Masu Haɗawa don Rarraba Hotunan Magani." MICCAI, 2015.
4. Vaswani, A., et al. "Kulawa Ne Duk Abin Da Kuke Bukata." NIPS, 2017.
5. Zhu, J., et al. "Fassarar Hotuna Zuwa Hotuna Marasa Biyu ta Amfani da Cibiyoyin Sadarwar Jijiya Masu Hamayya Masu Daidaitaccen Zagayowar." ICCV, 2017.
6. Kingma, D. P., & Ba, J. "Adam: Hanya don Ingantaccen Zaɓi." ICLR, 2015.