Adversarial Autoencoders
Yeshwanth Nagaraj
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Adversarial Autoencoders (AAEs) are a type of generative model that combine concepts from autoencoders and adversarial training. AAEs were introduced by Alireza Makhzani et al. in their paper titled "Adversarial Autoencoders" in 2015.
The main idea behind AAEs is to train an autoencoder in an adversarial setting, where the autoencoder is simultaneously trained to reconstruct input data and to generate samples that match a target distribution. This is achieved by introducing a discriminator network that tries to distinguish between the encoded latent space representations produced by the autoencoder and samples drawn from the target distribution.
The training process of AAEs involves two main steps:
By combining the reconstruction objective with the adversarial objective, AAEs learn to generate realistic samples that capture the underlying structure of the input data. The latent space representations learned by the autoencoder can be used for various tasks such as data generation, dimensionality reduction, and anomaly detection.
Adversarial Autoencoders have found applications in several domains, including image generation, text generation, speech synthesis, and anomaly detection. They provide a powerful framework for learning complex data distributions and generating high-quality samples from these distributions.
It's important to note that there have been subsequent advancements and variations of AAEs, such as conditional AAEs and Wasserstein Autoencoders, which further enhance the capabilities of adversarial learning in the context of autoencoders.
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Applications :
Python code :
Implementation of Adversarial Autoencoder -
import numpy as np
from keras.layers import Input, Dense, Lambda
from keras.models import Model
from keras.losses import binary_crossentropy
from keras import backend as K
from keras.datasets import mnist
# Load MNIST dataset
(x_train, _), (x_test, _) = mnist.load_data()
# Normalize and flatten images
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
# Define dimensions
input_dim = 784
latent_dim = 2
# Encoder network
inputs = Input(shape=(input_dim,))
hidden = Dense(256, activation='relu')(inputs)
z_mean = Dense(latent_dim)(hidden)
z_log_var = Dense(latent_dim)(hidden)
# Reparameterization trick
def sampling(args):
? ? z_mean, z_log_var = args
? ? epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0., stddev=1.)
? ? return z_mean + K.exp(z_log_var / 2) * epsilon
z = Lambda(sampling)([z_mean, z_log_var])
# Decoder network
decoder_hidden = Dense(256, activation='relu')
decoder_out = Dense(input_dim, activation='sigmoid')
hidden_decoded = decoder_hidden(z)
outputs = decoder_out(hidden_decoded)
# Discriminator network
discriminator_hidden = Dense(256, activation='relu')
discriminator_out = Dense(1, activation='sigmoid')
discriminator_input = discriminator_hidden(z)
validity = discriminator_out(discriminator_input)
# Define the models
encoder = Model(inputs, z_mean)
decoder = Model(inputs, outputs)
discriminator = Model(inputs, validity)
# Define loss functions
def vae_loss(inputs, outputs):
? ? reconstruction_loss = binary_crossentropy(inputs, outputs) * input_dim
? ? kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
? ? return reconstruction_loss + kl_loss
def discriminator_loss(real_output, fake_output):
? ? real_loss = binary_crossentropy(K.ones_like(real_output), real_output)
? ? fake_loss = binary_crossentropy(K.zeros_like(fake_output), fake_output)
? ? return real_loss + fake_loss
# Compile models
vae = Model(inputs, outputs)
vae.compile(optimizer='adam', loss=vae_loss)
discriminator.compile(optimizer='adam', loss=discriminator_loss)
# Training loop
epochs = 50
batch_size = 128
for epoch in range(epochs):
? ? for i in range(x_train.shape[0] // batch_size):
? ? ? ? # Train discriminator
? ? ? ? batch_images = x_train[i * batch_size: (i + 1) * batch_size]
? ? ? ? batch_latent = encoder.predict(batch_images)
? ? ? ? fake_latent = np.random.normal(size=(batch_size, latent_dim))
? ? ? ? d_loss_real = discriminator.train_on_batch(batch_latent, np.ones((batch_size, 1)))
? ? ? ? d_loss_fake = discriminator.train_on_batch(fake_latent, np.zeros((batch_size, 1)))
? ? ? ? d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
? ? ? ? # Train generator (VAE)
? ? ? ? g_loss = vae.train_on_batch(batch_images, batch_images)
? ? ? ? # Print progress
? ? ? ? print(f'Epoch: {epoch+1}/{epochs}, Batch: {i+1}/{x_train.shape[0] // batch_size}, D_loss: {d_loss}, G_loss: {g_loss}')
# Generate new samples
random_latent = np.random.normal(size=(10, latent_dim))
decoded_samples = decoder.predict(random_latent)
# Print generated samples
for i in range(10):
? ? generated_image = decoded_samples[i].reshape(28, 28) * 255
? ? generated_image = generated_image.astype('uint8')
? ? # Display or save the generated images as per your requirement
Please note that this is a simplified example to demonstrate the structure and training loop of an Adversarial Autoencoder. The code is based on the MNIST dataset and uses a basic architecture.