Machine learning using neural networks with multiple hidden layers to learn representations.
Hidden layers apply successive transformations to inputs. Nonlinear activations help networks represent more complex relationships. Layer count alone is not a measure of accuracy; architecture, data and training must be considered together.
Source: Official documentation.
Cross-attention is a specialized attention mechanism where computations are performed between two different data sequences.
A multimodal AI model developed by OpenAI that can represent text and images in the same vector space. Used as a prompt understanding layer in image generation tools.
A deep learning model that generates images by gradually denoising. It starts from random noise and step by step creates a meaningful image.
The attention mechanism is an AI component that allows neural networks to selectively focus on different parts of input data.
The process of converting text, images, or other data types into dense, fixed-size numerical vectors. Used for semantic similarity calculation and model input representation.
A deep learning model where two neural networks are trained against each other: a generator and a discriminator. The generator tries to produce realistic data, while the discriminator tries to distinguish between real and fake data.