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Creating Synthetic Pictures from Text utilizing RNN and CNN

Sandeep ., Veeresh Biradar, Gururaj Nase

Abstract


A fresh machine learning task is to synthesise textual descriptions into visual representations. Using a mix of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), one such method creates artificial images from text. RNNs, particularly those with Long Short-Term Memory (LSTM) units, absorb and comprehend the sequential structure of textual input. These networks collect contextual information and provide descriptive embeddings. These embeddings are used by a convolutional neural network (CNN) picture generating model to produce coherent and detailed pictures based on the abstract text attributes. Utilizing CNNs' capacity to produce contextually correct and high-resolution visual output, this hybrid technique enables advancements in automated design, virtual reality, and content production. Synthetic visuals that visually correspond with the given verbal descriptions are the end result.

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References


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