What is the difference in Encoding and Decoding in Generative AI

 

Aspect	Encoding	Decoding
Primary Function	Converts input data into a fixed-dimensional representation or embedding.	Generates output data or sequences based on a given representation.
Input	Takes raw data, such as text, images, audio, or other forms.	Receives a fixed-dimensional representation, often as a vector or tensor.
Focus	Learns to capture and abstract essential features or information from the input data.	Transforms the fixed-dimensional representation into human-readable or interpretable output data.
Direction	Typically a forward process, moving from raw data to a compact representation.	Usually a reverse process, taking a representation and producing data.
Models	Common models include Convolutional Neural Networks (CNNs) for images, Recurrent Neural Networks (RNNs) for sequential data, and Transformers for text.	Examples include Recurrent decoders in sequence-to-sequence models and Language models like GPT (Generative Pre-trained Transformer).
Use Cases	Feature extraction, data compression, and data representation.	Text generation, image generation, sequence prediction, and language translation.
Example Application	Encoding an image to a feature vector for image classification.	Decoding a language model to generate coherent paragraphs of text.
Data Dimensionality	Typically reduces data dimensionality for efficient representation.	Often increases data dimensionality for generating expressive content.
Notable Technologies	Autoencoders, VAEs (Variational Autoencoders), CNNs for encoding images.	RNNs, Transformers, and GANs for decoding and generating content.