An approach to developing models that learn patterns from data for prediction or content generation.
Supervised learning uses labeled examples, while unsupervised learning finds structure in unlabeled data. Design applications include image classification and content generation. Results depend on the data and how the model is evaluated.
Source: Official documentation.
The FID Score is a standard evaluation metric used to measure the quality and realism of generated images.
The tendency of AI models to generate non-real, fabricated, or incorrect information/content. It manifests differently in text, image, and video generation.
The process where a trained AI model makes predictions or generates output on new inputs. In image generation, it corresponds to converting a prompt into an image.
A text command that defines unwanted elements in AI image generation. The model generates images while avoiding the elements specified in the negative prompt.
A text-based instruction or command given to AI models. It is used to describe the desired output and guides the model's generation process.
A discipline that encompasses techniques and strategies for writing prompts to get the best results from AI models. It includes proper word choice, structuring, and parameter usage.