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What is the difference in Encoding and Decoding in Generative AI

 

AspectEncodingDecoding
Primary FunctionConverts input data into a fixed-dimensional representation or embedding.Generates output data or sequences based on a given representation.
InputTakes raw data, such as text, images, audio, or other forms.Receives a fixed-dimensional representation, often as a vector or tensor.
FocusLearns to capture and abstract essential features or information from the input data.Transforms the fixed-dimensional representation into human-readable or interpretable output data.
DirectionTypically a forward process, moving from raw data to a compact representation.Usually a reverse process, taking a representation and producing data.
ModelsCommon 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 CasesFeature extraction, data compression, and data representation.Text generation, image generation, sequence prediction, and language translation.
Example ApplicationEncoding an image to a feature vector for image classification.Decoding a language model to generate coherent paragraphs of text.
Data DimensionalityTypically reduces data dimensionality for efficient representation.Often increases data dimensionality for generating expressive content.
Notable TechnologiesAutoencoders,
VAEs (Variational Autoencoders),
CNNs for encoding images.		RNNs, Transformers, and

GANs for decoding and generating content.
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