most citedFace Generation from Textual Features using Conditionally Trained Inputs to Generative Adversarial Networks

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.LG2026

Rapid Augmentations for Time Series (RATS): A High-Performance Library for Time Series Augmentation

Wadie Skaf, Felix Kern, Aryamaan Basu Roy +3

Time series augmentation is critical for training robust deep learning models, particularly in domains where labelled data is scarce and expensive to obtain. However, existing augm…

cs.CV2023★ 1 cited

Face Generation from Textual Features using Conditionally Trained Inputs to Generative Adversarial Networks

Sandeep Shinde, Tejas Pradhan, Aniket Ghorpade +1

Generative Networks have proved to be extremely effective in image restoration and reconstruction in the past few years. Generating faces from textual descriptions is one such appl…