Generative A.I. & Model Photography
This endeavor started as a simple experiment, but unexpectedly, I found myself completely engrossed in it. Although I might revisit photography in the future, for now, these are images enriched using generative A.I., adding depth and a context that wasn't present when the photos were originally taken. Importantly, the models in these photos remain untouched from the initial shot, with only the surrounding environment being modified.
For the sake of comparison, the original photographs are showcased with accompanying captions.
Enjoy
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*Generative AI is a subfield of artificial intelligence that focuses on the development of systems capable of creating new, original content. These systems generate output, such as text, images, music, or other types of content, based on the patterns they learn from input data.
The most common types of generative AI include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Recurrent Neural Networks (RNNs).
1. GANs consist of two parts: a generator network, which creates new data instances, and a discriminator network, which tries to distinguish between real and artificial data. The two networks are trained together, with the generator network trying to produce data that the discriminator network will believe is real.
2. VAEs are a type of autoencoder, a neural network trained to replicate its input data to its output, while also learning a compressed representation of that data in the middle (hidden) layer. VAEs, however, add an element of probability to their encoding and decoding processes, meaning that they can generate new data instances by sampling from the learned data distribution.
3. RNNs are a class of neural networks that are excellent at predicting what comes next in a sequence, making them ideal for tasks such as text and speech generation.
Generative AI has numerous applications, ranging from creating art and music to designing drugs, and even generating realistic, synthetic data for training other AI models.
