Image generation refers to the process of generating new images from a given dataset or learned patterns. This is achieved using various techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), and other deep learning architectures.

Convolutional Neural Networks (CNNs) are used extensively in image generation tasks. CNNs are deep learning models that can learn hierarchical representations of images. They consist of multiple layers of convolutional and pooling operations, followed by fully connected layers. The convolutional layers learn local features from the input images, while the pooling layers downsample the feature maps. CNNs are capable of handling large volumes of image data and are used in various image generation tasks.

Generative Adversarial Networks (GANs) are a popular class of deep learning models used for image generation. GANs consist of two neural networks - a generator network and a discriminator network. The generator network generates new images, while the discriminator network tries to distinguish between the generated images and the real ones. The two networks are trained together in an adversarial manner, where the generator tries to generate images that can fool the discriminator, and the discriminator tries to correctly classify the generated and real images. GANs have been used to generate realistic images in various domains, including computer vision, art, and fashion.

Variational Autoencoders (VAEs) are another class of deep learning models used for image generation. VAEs are based on the idea of encoding an input image into a lower-dimensional latent space, and then decoding it back into an output image. The encoder and decoder networks are trained together to minimize the reconstruction loss between the input and output images. VAEs can also generate new images by sampling from the latent space. VAEs have been used to generate images in various domains, including computer vision, art, and music.

• Style Transfer
• Neural Style Transfer
• Deep Dream
• Super Resolution
• Image Captioning
• Image Segmentation

Image generation is a rapidly evolving field, with new techniques and architectures being developed regularly. Researchers are exploring new ways to generate images that are more realistic and diverse. Image generation has many practical applications, including in computer vision, art, and entertainment.