Generative Adversarial Networks (GANs) are powerful unsupervised methods for learning to perform complex tasks like image synthesis, image-to-image translation, and text-to-image tasks. Image-to-image translation is seen as a special case of the GAN model and is particularly useful for dealing with unsupervised tasks, i.e. those where the labels are not available. This application of GANs to unsupervised image-to-image translation has recently become quite promising and has attracted a lot of attention in the fields of vision, robotics and AI.
Image-to-image translation is an important task in computer vision, as it can be used to map one image modality into another. For example, it can be used to convert a photo into a painting, a line drawing into a cartoon, and so on. As GANs are unsupervised models, this type of task is particularly suited for them.
GANs can be used to generate the target image from a source image without any labeled data. GANs are essentially built up as two neural networks, an encoder and a decoder, trained in a competitive way. The encoder takes a source image and converts it into a reduced representation called a latent space. The decoder then takes this latent space representation and reconstructs the image in the target domain. To train the GAN, the encoder learns the latent space representation of the source image, and the decoder learns the target domain representation.
The training procedure is based on a minimization process with GANs, making them particularly well-suited for unsupervised tasks, since they do not require labeled data. The goal is to minimization a loss function which is a combination of a reconstruction loss, which measures how accurately the decoder was able to reconstruct the image, and a discriminator loss, which measures how accurately the discriminator was able to distinguish between the generated images and real images.
Recently, research has been conducted on how to increase the performance of GANs for generic image-to-image translation. Such efforts have been shown to improve the results and increase the quality of the generated images.
#We use the Keras library to build a GAN for image-to-image translation import keras from keras.layers import Input, Dense, Reshape from keras.layers import BatchNormalization, Activation from keras.layers import Conv2DTranspose, UpSampling2D from keras.layers import LeakyReLU from keras.layers import Dropout #We define our encoder network def define_encoder(): #Input image in_image = Input(shape=(256,256,3)) #Input image size is 256*256 #Encoder model = Conv2D(128, (3,3), strides=(2,2), padding='same')(in_image) model = LeakyReLU(alpha=0.2)(model) model = Conv2D(128, (3,3), strides=(2,2), padding='same')(model) model = LeakyReLU(alpha=0.2)(model) model = Conv2D(128, (3,3), strides=(2,2), padding='same')(model) model = LeakyReLU(alpha=0.2)(model) model = Conv2D(128, (3,3), strides=(2,2), padding='same')(model) encoder = LeakyReLU(alpha=0.2)(model) return Model(in_image, encoder) #We define our decoder network def define_decoder(encoder): #Decoder model = Conv2DTranspose(128, (3,3), strides=(2,2), padding='same')(encoder) model = LeakyReLU(alpha=0.2)(model) model = Conv2DTranspose(128, (3,3), strides=(2,2), padding='same')(model) model = LeakyReLU(alpha=0.2)(model) model = Conv2DTranspose(128, (3,3), strides=(2,2), padding='same')(model) model = LeakyReLU(alpha=0.2)(model) model = Conv2DTranspose(128, (3,3), strides=(2,2), padding='same')(model) model = UpSampling2D()(model) decoder = Activation('tanh')(model) return Model(encoder, decoder) #We define our GAN def define_gan(generator, discriminator): #Combine the two models model = Sequential() model.add(generator) model.add(discriminator) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) return model
In summary, GANs can be used for unsupervised image-to-image translation, and research has been conducted to make them more efficient for this task. GANs are composed of two neural networks, an encoder and a decoder, which are trained in a competitive manner. The encoder takes a source image and converts it into a reduced representation in the latent space, while the decoder takes this latent space representation and generates an image in the target domain. GANs can then be used to generate the target image from a source image without any labeled data. With recent improvements to their performance, GANs are emerging as a powerful tool for image-to-image translation.