Overview of SkipGANomaly, Algorithm and Example Implementation

Overview of SkipGANomaly

SkipGANomaly is a GAN-based method described in “Overview of GANs and Various Applications and Implementations” for the purpose of anomaly detection, which improves on regular GANomaly by introducing skip connections. SkipGANomaly is a GAN-based method that improves on regular GANomaly by introducing skip connections to improve anomaly detection performance.

Features of SkipGANomaly include the following

1. Specialized anomaly detection: A method designed to automatically detect anomalies (defects, foreign objects, defective products, etc.) in images.
2. Encoder-Decoder Structure + Skip Connections: While regular GANomaly has an Encoder-Decoder-Encoder (EDE) structure, SkipGANomaly adds skip connections to retain more detailed information.
3. More accurate reconstruction: While detailed image reconstruction is difficult with regular GANomaly, SkipGANomaly can reconstruct images while preserving detailed features.
4. Improved learning and anomaly score: SkipGANomaly is more stable in anomaly score calculation than conventional GANomaly, enabling more accurate anomaly detection. Anomalies are detected by learning on normal data and scoring the difference from anomaly data.

The architecture of SkipGANomaly is based on the following autoencoder + GAN configuration.

1. Encoder: Converts the input image into a feature map and compresses it into a latent vector (latent representation).
2. Decoder: Reconstructs the image based on the latent vectors from the encoder. Uses skip connections to transmit more detailed features to the decoder.
3. Second Encoder: The reconstructed image is passed through the encoder again to generate feature vectors. This is compared to the first encoder’s feature vector and an anomaly score is calculated.
4. Discriminator: acts as a discriminator for the GAN to determine whether the reconstructed image is real (normal) or fake (abnormal).

SkipGANomaly has a skip connection, which allows the encoder’s feature map to be transmitted directly to the decoder, enabling reconstruction that preserves fine information.

Advantages of SkipGANomaly include

• Highly accurate anomaly detection: Anomaly scores are calculated more accurately than with normal GANomaly.
• Retention of information by skip splicing: Anomaly detection performance is improved because detailed features can be reconstructed while retaining their original features.
• Unsupervised learning: Only normal data is used for learning, allowing anomaly detection without using anomaly data.
• Real-time applicability: applicable to anomaly detection in industrial applications (e.g., manufacturing, medical, surveillance, etc.)

SkipGANomaly is a method that improves anomaly detection accuracy by introducing skip connections to conventional GANomaly, and is applied in a wide range of fields such as manufacturing, medicine, and security, and is capable of highly accurate anomaly detection through unsupervised learning.

Implementation Example

An example implementation of SkipGANomaly can proceed as follows. Here is a basic implementation flow using PyTorch.

Implementation Assumptions: The implementation of SkipGANomaly requires the following structure: Generator, Discriminator, and Encoder-Decoder.

• The Encoder compresses the input image into a low-dimensional latent space.
• The Decoder reconstructs the image from the latent vectors.
• Skip Connection allows the output of the Encoder’s middle layer to be directly transmitted to the Decoder.
• Discriminator identifies whether the generated image is real (normal) or fake (abnormal).

Required Libraries

pip install torch torchvision

A simple example implementation of SkipGANomaly: The following is a simple skeleton code of a SkipGANomaly implementation.

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

# Encoder Definition
class Encoder(nn.Module):
    def __init__(self):
        super(Encoder, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)
        self.fc1 = nn.Linear(128 * 8 * 8, 256)
    
    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = torch.relu(self.conv2(x))
        x = x.view(x.size(0), -1)  # Flatten the tensor
        x = torch.relu(self.fc1(x))
        return x

# Decoder Definition
class Decoder(nn.Module):
    def __init__(self):
        super(Decoder, self).__init__()
        self.fc1 = nn.Linear(256, 128 * 8 * 8)
        self.deconv1 = nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1)
        self.deconv2 = nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = x.view(x.size(0), 128, 8, 8)  # Reshape back to image-like tensor
        x = torch.relu(self.deconv1(x))
        x = torch.sigmoid(self.deconv2(x))  # Output image
        return x

# SkipGANomaly Generator (Encoder + Decoder + Skip connection)
class SkipGANomaly(nn.Module):
    def __init__(self):
        super(SkipGANomaly, self).__init__()
        self.encoder = Encoder()
        self.decoder = Decoder()
    
    def forward(self, x):
        # Skip connection to send encoder output to decoder
        z = self.encoder(x)
        reconstructed_x = self.decoder(z)
        return reconstructed_x

# Definition of Discriminator
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)
        self.fc1 = nn.Linear(64 * 8 * 8, 1)
    
    def forward(self, x):
        x = torch.relu(self.conv1(x))
        x = x.view(x.size(0), -1)  # Flatten the tensor
        x = torch.sigmoid(self.fc1(x))  # Probability of being "real"
        return x

# Model initialization
model = SkipGANomaly()
discriminator = Discriminator()

# Loss functions and optimization methods
criterion = nn.MSELoss()  # reconfiguration loss
optimizer_g = optim.Adam(model.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))

# Load dataset (CIFAR-10 dataset is used here)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

# learning loop
for epoch in range(10):
    for i, (real_images, _) in enumerate(train_loader):
        # normalization
        real_images = real_images.cuda()

        # Reconstruction by generation (decoder)
        reconstructed_images = model(real_images)

        # Discrimination by Discriminator
        real_labels = torch.ones(real_images.size(0), 1).cuda()
        fake_labels = torch.zeros(real_images.size(0), 1).cuda()

        # Discriminator Update
        optimizer_d.zero_grad()
        real_output = discriminator(real_images)
        fake_output = discriminator(reconstructed_images.detach())
        d_loss = torch.mean((real_output - real_labels) ** 2) + torch.mean((fake_output - fake_labels) ** 2)
        d_loss.backward()
        optimizer_d.step()

        # Generator (model) updates
        optimizer_g.zero_grad()
        fake_output = discriminator(reconstructed_images)
        g_loss = torch.mean((fake_output - real_labels) ** 2) + criterion(reconstructed_images, real_images)
        g_loss.backward()
        optimizer_g.step()

        # Show Log
        if i % 100 == 0:
            print(f"Epoch [{epoch}/{10}], Step [{i}/{len(train_loader)}], D Loss: {d_loss.item()}, G Loss: {g_loss.item()}")

# Saving the model
torch.save(model.state_dict(), 'skipganomaly.pth')

Description

• Encoder: Compresses an image into a latent vector.
• Decoder: Reconstructs the image based on the latent vectors. A skip connection sends information from the encoder to the decoder for more detailed reconstruction.
• Discriminator: determines whether the generated image is real or fake, and the generator learns to aim to fool the discriminator.

Application Examples

Application examples of SkipGANomaly are described below.

1. abnormality detection in the manufacturing industry: In quality control in the manufacturing industry, it is important to detect abnormalities in products manufactured on the production line. SkipGANomaly can learn data from normal products and effectively detect abnormalities (e.g., scratches and defects) that occur during manufacturing.

• Example: SkipGANomaly is used to identify normal and abnormal parts in the manufacturing process of automobile body parts (e.g., doors, car bodies) and electronics parts (e.g., smartphone screens).
• Operation: Using images and sensor information from the production line as input, SkipGANomaly compares them with normal products to detect signs of abnormality. When an abnormality is detected, an alert is immediately issued and corrective action is taken. 2.

2. anomaly detection in medical imaging: In medical image analysis (e.g., X-ray, CT scan, MRI, etc.), early detection of patient anomalies can be directly related to life, and SkipGANomaly can be used to learn normal anatomy and identify anomalies (e.g., tumors, lesions, abnormal tissue) Example.

• Example: Using image data from CT scans, normal visceral structures and abnormal lesions (e.g., tumors, blood clots, etc.) can be detected.
• Operation: Medical image data is input into SkipGANomaly, and the reconstructed image is compared with the original image to detect abnormal areas. This allows physicians to quickly recognize abnormalities and suggest appropriate treatment.

3. security monitoring using surveillance cameras: SkipGANomaly is also effective in the field of anomalous behavior detection using surveillance camera images and video data, for example, it can be used to detect anomalous movements (vandalism, intrusion of suspicious persons) at an early stage.

• Example: Detecting behavior that differs from normal walking (e.g., violent behavior, theft, appearance of suspicious persons) from surveillance camera footage.
• Operation: The system learns normal video from surveillance cameras and abnormal behavior, and sends real-time alerts when abnormalities occur. 4.

4. fraud detection in the financial sector: SkipGANomaly can also be applied to fraud detection in the financial sector. To identify abnormal transactions (e.g., fraudulent transfers or card usage), it learns patterns of normal transactions and detects abnormal behavior.

• Example: Detect fraudulent transactions using credit card usage history and online banking transaction logs.
• Operation: Learning normal transaction history and issuing warnings when there are abnormal transactions (e.g., sudden increases in transaction amounts, frequent overseas transfers, etc.).

5. network security: SkipGANomaly can also be used to detect anomalies in network traffic, e.g., to identify abnormal accesses or attacks (e.g., DoS attacks, intrusion detection) in the network.

• Example: Distinguish between normal access patterns and unauthorized access (e.g., DDos attacks) in server and network communication logs.
• Operation: Input network traffic into SkipGANomaly to detect abnormal patterns (e.g., sudden increase in traffic) and initiate immediate response. 6.

6. product failure prediction: In the manufacturing and automotive industries, SkipGANomaly can also be used to predict product failures, enabling early detection and preventive maintenance when anomalous behavior indicates a product failure.

• Example: Based on machine operation data and sensor information, detect normal operation patterns and signs of abnormality (e.g., overheating and wear).
• Operation: Analyze machine operation data, issue warnings when abnormalities are detected, and perform preventive maintenance work.

reference book

Reference books on SkipGANomaly are described.

1. “Generative Adversarial Networks

Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville
Description: A standard textbook on GANs that covers both the basics and the state-of-the-art of GANs, SkipGANomaly leverages GANs, so the fundamentals learned in this book are useful.

2. “Deep Learning with Python

Author: François Chollet
Description: A book on deep learning by François Chollet, the creator of Keras, with chapters on GANs and anomaly detection, and many implementation examples to give you the basic skills to implement SkipGANomaly.

3. “Anomaly Detection: A Survey

Authors: Chandola, V., Banerjee, A., & Kumar, V.
Abstract: A comprehensive review paper on anomaly detection; since SkipGANomaly is one of the anomaly detection methods, you can learn the basics of anomaly detection and other methods in this paper.

4. “Hands-On Generative Adversarial Networks with Keras

Author: Rafael Valle
Description: A practical guide to implementing GANs in Keras, a useful resource for learning about GAN applications such as SkipGANomaly.

5. “Deep Learning for Anomaly Detection

Authors: Ankit B. Ghosh, Ritu Arora
Description: A deep learning book focused on anomaly detection, a good resource for learning deep learning based anomaly detection methods such as SkipGANomaly.

References

Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection.

Author(s): Samet Akcay, Amir Atapour-Abarghouei, Toby P. Breckon
Publication Year: 2019